MCP Server Directory

Ren Systems provides an AI-powered sales intelligence platform designed for dealmakers and client-facing teams in industries like commercial real estate, executive search, private equity, investment banking, and management consulting. The company's mission is to transform professional relationships into deals by surfacing revenue-generating signals from millions of paywalled news and data sources in real-time. This MCP server exposes Ren's relationship intelligence capabilities, allowing users to access their business network of contacts and companies, retrieve curated news feeds, and prepare for upcoming meetings. The tools are organized into several categories: **contact and company lookup**, **calendar and meeting management**, **curated alert feeds** (Main, VIP, Meetings, and Custom), and **targeted news retrieval** for both tracked and external people and companies. Together, these tools help professionals stay informed about the people and organizations that matter most to them, automate research and meeting prep, and identify winnable opportunities before the competition. ## Use Cases **1. Contact and Company Research** Search your business network to find specific people or organizations, then dive into their full profiles for detailed context. - Sample prompt: `Find the contact record for Sarah Chen who works at Goldman Sachs and show me her full profile details.` **2. Meeting Preparation** Retrieve upcoming meetings within a time range and pull contextual news on attendees to walk in prepared. - Sample prompt: `What meetings do I have next week, and give me a briefing on each attendee from Blackstone I'll be meeting with.` **3. VIP Monitoring** Stay on top of the highest-signal news about the contacts and companies you've flagged as most important. - Sample prompt: `Show me the 15 most important VIP alerts about my key clients from this week.` **4. Daily News Digest via Main Feed** Scan the most important recent news across your entire business network in one place. - Sample prompt: `Give me the top 30 alerts from my main feed and summarize the biggest developments I should act on today.` **5. Deep-Dive on a Specific Contact or Company** Fetch recent news about a tracked contact or company to spot outreach triggers like job changes, funding, or awards. - Sample prompt: `Pull all news from the last 30 days about Acme Corp (company in my network) so I can find a reason to reconnect.` **6. Researching External People and Companies** Get news about people or organizations not yet in your network, useful for prospecting new relationships. - Sample prompt: `I'm meeting a new prospect named Michael Torres, CFO at Stripe, next week — what recent news is there about him and the company?` **7. Custom Feed Review** List your self-configured alert streams and fetch their alerts to track specific themes, sectors, or accounts. - Sample prompt: `List my custom feeds, then show me the latest 20 alerts from my "Private Equity M&A" feed.` **8. Targeted Meeting Intelligence** Use the curated Meetings Feed to get contextual news and updates about people you're scheduled to meet. - Sample prompt: `Fetch my meetings feed and highlight any news that could impact my upcoming client conversations.` **9. Filtered Meeting Search** Use regex filtering to find specific meetings by attendee, company domain, or location. - Sample prompt: `Find all meetings in the next two weeks that involve anyone with a @salesforce.com email address.` **10. Combined Prospecting Workflow** Blend network search, news retrieval, and external research to build a complete outreach strategy. - Sample prompt: `Find everyone in my network at JLL, pull recent news about the company, and draft talking points for a partnership pitch.`

This MCP server is provided by **Collate**, the company behind **OpenMetadata** — the open-source standard for data context, semantics, and metadata used by thousands of enterprise deployments. The server acts as a bridge between LLMs and your OpenMetadata/Collate instance, exposing your organization's data catalog, governance framework, and data quality systems through conversational AI. The tools fall into several core categories: **data discovery** (keyword and semantic search, entity detail retrieval), **lineage and impact analysis** (dependency graphs, root cause analysis, lineage creation), and **governance and metadata management** (creating glossaries, terms, tags, classifications, domains, data products, and metrics). It also supports **data quality operations** (test definitions and test cases) and **user context awareness** (identity, ownership, and team roles). Together, these tools let AI agents ground their answers in trusted business context — enabling accurate discovery, documentation, classification, and quality monitoring across your entire data estate, all backed by OpenMetadata's open context layer. ## Use Cases **1. Discovering data assets by concept or keyword** Combine semantic and keyword search to help users find the right tables, dashboards, or pipelines even when they don't know exact names. - Sample prompt: `Find all BigQuery tables owned by the marketing team that contain customer spending or purchase history data.` **2. Exploring entity details and schema** Retrieve full descriptions, columns, tags, owners, and custom properties for a specific asset. - Sample prompt: `Show me the full column details and description for the table analytics.prod.customer_orders.` **3. Root cause and impact analysis** Use lineage and dedicated root-cause tooling to diagnose why a data quality issue occurred and what downstream assets are affected. - Sample prompt: `The dashboard "Daily Revenue Report" looks wrong. Run a root cause analysis to find any upstream data quality failures and tell me what downstream assets are impacted.` **4. Tracing dependencies for a table** Explore upstream sources and downstream consumers to understand data flow before making changes. - Sample prompt: `Show me the upstream and downstream lineage for the table warehouse.sales.fct_transactions, going 3 hops in each direction.` **5. Building and maintaining a business glossary** Create glossaries and hierarchical glossary terms to standardize business vocabulary. - Sample prompt: `Create a glossary called "Finance" and add a term "Net Revenue" with a description explaining it's total revenue minus returns and discounts.` **6. Setting up data quality tests** Look up available test definitions and create table- or column-level test cases to monitor data reliability. - Sample prompt: `Add a data quality test on the "email" column of customers.prod.users to ensure values are not null, and a test that the table row count stays above 1000.` **7. Governance: classifications, tags, and PII management** Create classifications and tags to label sensitive data across your assets. - Sample prompt: `Create a classification called "PII" that is mutually exclusive, then add a tag "Sensitive" under it for tagging personal data columns.` **8. Organizing data into domains and data products** Establish governance groupings by creating domains and data products tied to business value. - Sample prompt: `Create a "Customer 360" data product under the Marketing domain and describe it as the unified view of customer profiles and engagement.` **9. Bulk metadata enrichment via patching** Update descriptions, owners, tags, or domain assignments on existing entities. - Sample prompt: `Assign the "Finance" domain and set the owner to the data-engineering team on the table warehouse.finance.gl_entries.` **10. Personal context and ownership review** Answer identity questions and surface assets that need attention from the current user. - Sample prompt: `What teams am I on, what's my role, and which of the tables I own are missing descriptions or a tier?` **11. Registering business metrics/KPIs** Define measurable KPIs with expressions, granularity, and units for governance and reuse. - Sample prompt: `Create a metric called "DailyActiveUsers" as a SQL COUNT with daily granularity that counts distinct user_id from events.prod.sessions.` **12. Creating lineage relationships manually** Explicitly connect two assets where automated lineage is missing. - Sample prompt: `Create a lineage relationship showing that the pipeline "etl_orders" feeds into the table warehouse.sales.fct_orders.` **13. Auditing data quality across the catalog** Search test cases and test suites to review the health of monitored assets. - Sample prompt: `List all failing test cases in the sales database and show me the latest results for each.`

Sequenzy is an AI-native email marketing platform built primarily for SaaS companies and e-commerce stores. Its MCP server exposes a comprehensive toolset that lets AI assistants manage nearly every aspect of an email marketing operation—from company setup and sending-domain verification to subscriber management, campaign creation, automation sequences, and revenue analytics. The platform emphasizes fast, AI-driven setup and native integrations with Stripe, Shopify, WooCommerce, and other billing/store providers. The tools cover a broad range of functional categories. These include **account & company management** (companies, brand context, email themes, sender profiles, API keys), **subscriber & audience management** (contacts, imports, tags, lists, segments, notes, events), and **content creation** (templates, campaigns, transactional emails, landing pages, signup forms, image assets, AI-generated content). Beyond content, the server offers deep **automation capabilities** through sequences—drip flows with triggers, delays, branching logic, discounts, SMS steps, and webhooks—plus **A/B testing**, **integrations** (payment, e-commerce, analytics, ad platforms), and **Meta custom audience syncs**. Finally, it provides robust **analytics & deliverability** tools (campaign/sequence stats, per-email metrics, email send inspection, tracking settings, suppression management), an **inbox/conversations** system for reply handling, **outbound webhooks**, **team management**, and **SMS/MMS** messaging. ## Use Cases **1. Onboard a new brand and configure sending** Set up a company from its domain, let the platform extract brand info, then add and verify a sending domain with DNS records. - Sample prompt: `Create a new company for acme.io, then add mail.acme.io as a sending domain and show me the DNS records I need to publish.` **2. Build an AI-generated welcome sequence** Generate a multi-step nurture flow triggered when new contacts are added, then review before activating. - Sample prompt: `Create a 4-email welcome sequence for new SaaS trial users over 10 days that explains our core features and encourages upgrading. Don't enable it yet.` **3. Recover failed payments with dunning automation** Connect a payment provider and build an event-triggered sequence that emails customers when a payment fails. - Sample prompt: `Set up a dunning sequence that triggers on the invoice.payment_failed event and sends 3 reminder emails asking the customer to update their card.` **4. Segment high-value users and launch a campaign** Create a dynamic segment based on tags or custom attributes, then send a targeted campaign to it. - Sample prompt: `Create a segment of active customers with MRR over $50, then draft and schedule a campaign announcing our new enterprise features to them next Tuesday at 10am.` **5. Run abandoned cart recovery for a Shopify store** Verify the storefront pixel is live and configure cart/browse abandonment automation settings. - Sample prompt: `Check whether our Shopify tracking pixel is installed, activate it if needed, and set cart abandonment to fire after 2 hours of inactivity.` **6. Migrate subscribers from another platform** Bulk-import CRM-grade contact records with names, tags, statuses, and original signup dates preserved. - Sample prompt: `Import this list of 3,000 subscribers from our SendGrid export, preserving their original signup dates and adding them to the "Newsletter" list without triggering welcome sequences.` **7. Set up an A/B test on a campaign** Create subject-line or content variants for a draft campaign and configure winner criteria. - Sample prompt: `Create an A/B test on my draft launch campaign with 3 subject line variants, test on 20% of the audience for 4 hours, and pick the winner by open rate.` **8. Analyze campaign and sequence performance** Pull detailed stats including opens, clicks, replies, conversions, and revenue attribution. - Sample prompt: `Show me the stats for my last product launch campaign, including revenue attributed, click breakdown by link, and reply rate.` **9. Add SMS steps to an automation** Check SMS readiness, generate message variants, and insert an SMS step into an existing sequence. - Sample prompt: `Check if SMS is enabled on my account, then add an SMS reminder step to my cart recovery sequence 6 hours after the first email.` **10. Create transactional emails and send via API** Build a saved transactional template with a slug and send personalized one-off emails. - Sample prompt: `Create a password-reset transactional email template with a reset button, then send a test to me@example.com.` **11. Set up integration for a custom app via MCP/CLI** Get integration code examples and create an API key scoped for an AI agent. - Sample prompt: `Show me the Next.js code to add subscribers from my signup form, and create an agent_safe API key I can put in my .env file.` **12. Manage conversations and replies** Triage inbound subscriber replies from the team inbox and respond or close threads. - Sample prompt: `List all unread open conversations, then reply to the one from jordan@example.com letting them know their issue is resolved and close it.` **13. Sync a segment to Meta Ads** Push a saved or predefined segment to a Meta custom audience on a schedule for retargeting. - Sample prompt: `Sync my "high spenders" segment to a Meta custom audience called "Retargeting - VIP" on our main ad account, refreshed daily.` **14. Diagnose deliverability and suppression issues** Inspect tracking settings, check recipient suppression, and clear stale bounce blocks. - Sample prompt: `Check why opens aren't being recorded for our sends, and find out if customer@example.com is suppressed by a bounce and clear it if so.` **15. Set up outbound webhooks for real-time events** Create a webhook endpoint subscribed to delivery and engagement events, with signature verification. - Sample prompt: `Create a webhook pointing to https://myapp.com/hooks/sequenzy subscribed to email.bounced and email.complained events, and send a test event.`

Brew is an AI-native Email Service Provider (ESP) that rebuilds the entire email marketing workflow—strategy, copywriting, design, segmentation, delivery, and analysis—around AI. This MCP server exposes the full Brew toolbelt to any MCP client (ChatGPT, Claude, Grok, Cursor, and more), so an agent can generate on-brand emails, send campaigns, build automations, manage contacts, and analyze performance backed by the same brand and data as the Brew app. The server offers a comprehensive set of tools spanning several categories: **email design** (AI generation, editing, importing from HTML/MJML/JSX/Figma, cloning, versioning, and exporting to third-party ESPs), **quality assurance** (accessibility audits, cross-client rendering previews, and inbox-placement/spam testing), and **sending** (one-off campaigns, test sends, scheduled and gradual/warmup delivery with pause/resume/cancel controls). It also covers **audience and contact management** (saved audience filters, contact CRUD, CSV import, email validation, and custom fields), **automations** (visual workflow graphs with triggers, waits, filters, and splits, plus event triggers and manual-audience runs), **domains and deliverability** (registering, verifying, and monitoring sending-domain health), and **analytics** (overview dashboards, per-campaign, per-send, per-automation, and per-recipient event feeds). Rounding it out are **brand and creative assets** tools—brand identity management, image search/generation, GIF creation, image transformation, and a template gallery. ## Use Cases ### Email Design & Creation - **Generate an on-brand email from a prompt** — Brew learns your palette, typography, and voice to draft a ready-to-send design. - Sample prompt: `Create a promotional email for our Black Friday sale — 30% off all annual plans, ending Nov 30, aimed at trial users who haven't upgraded.` - **Build an email from existing web content** — ground a design in real URLs like a blog recap or product roundup. - Sample prompt: `Turn our latest three blog posts into a monthly newsletter — here are the URLs. Keep it skimmable with a CTA to each article.` - **Import a Figma frame as an editable email** — convert a designed frame into responsive React Email JSX. - Sample prompt: `Import this Figma frame into Brew as an email design: https://figma.com/design/abc?node-id=12-345` - **Edit an existing design with natural language** — make scoped, in-place changes without a full rewrite. - Sample prompt: `In my "Spring Launch" email, swap the hero for the garden campaign image and tighten the CTA copy to two words.` ### QA & Deliverability Testing - **Audit accessibility before sending** — check WCAG 2.1 issues like contrast, alt text, and heading structure. - Sample prompt: `Run an accessibility audit on my "Welcome Series #1" email and list what I need to fix.` - **Preview across real inboxes and devices** — see how a design renders in Gmail, Outlook, Apple Mail, iOS, and dark mode. - Sample prompt: `Show me how my newsletter renders in Outlook 2021, Gmail dark mode, and Apple Mail on iOS.` - **Run an inbox placement test** — check whether a design lands in inbox vs spam across mailbox providers. - Sample prompt: `Run a spam placement test for my "Q1 Announcement" email from our marketing domain across Gmail, Outlook, and Yahoo.` ### Sending & Campaigns - **Send a campaign to a saved audience** — with confirmation and scheduling controls. - Sample prompt: `Schedule my "December Newsletter" to the "Active US Subscribers" audience for Dec 5 at 9am, subject line "Your December Highlights."` - **Warm up a domain with gradual delivery** — start small and grow each batch. - Sample prompt: `Send my re-engagement campaign gradually — start at 10% and increase 10% every 6 hours in America/New_York.` - **Cancel or pause an in-flight send** — stop delivery before or during a send. - Sample prompt: `Pause the gradual send I just started, then cancel it entirely.` ### Audiences & Contacts - **Create a segment in plain English** — Brew translates intent into a live filter. - Sample prompt: `Create an audience of contacts active in the last 30 days who opened an email and are in the US or EU.` - **Import and validate contacts** — bulk-load a CSV and check deliverability. - Sample prompt: `Import this CSV of contacts, mapping "Email Address" to email and "First" to firstName, and validate all addresses first.` - **Search and clean up your list** — find and batch-remove contacts. - Sample prompt: `Find all contacts who bounced in the last 60 days and delete them.` ### Automations & Triggers - **Build a lifecycle automation** — welcome, abandoned cart, or re-engagement flows on a visual canvas. - Sample prompt: `Build a 3-email welcome flow: send email 1 immediately, wait 2 days, then send email 2 only to people who opened the first.` - **Create and fire an event trigger** — wire product events to automated emails. - Sample prompt: `Create a "purchase_completed" trigger with fields for email, orderTotal, and productName, then fire it as a test with sample data.` - **Test an automation end-to-end** — QA a flow by delivering to your own inbox. - Sample prompt: `Test my abandoned-cart automation and send the emails to me at test@mycompany.com so I can check them.` ### Analytics & Reporting - **Get a performance overview** — windowed sends, opens, clicks, and rates. - Sample prompt: `Show me our email analytics for the last 30 days — sent, delivered, open rate, and click rate.` - **Compare campaign KPIs** — lifetime metrics across all campaigns. - Sample prompt: `Which of my campaigns had the highest click rate this quarter?` - **Trace a recipient's event history** — per-recipient sent/opened/clicked feed. - Sample prompt: `Show me every email event for jane@acme.com over the past 90 days.` ### Domains & Deliverability Health - **Add and verify a sending domain** — register DNS and confirm auth. - Sample prompt: `Add newsletter.mycompany.com as a sending domain and tell me which DNS records I need to set up.` - **Check domain health** — get a verdict with actionable fixes. - Sample prompt: `Check the deliverability health of our main sending domain and flag anything at risk.` ### Creative Assets & Brand - **Generate on-brand imagery** — produce hero images or ad creatives from a prompt. - Sample prompt: `Generate a 16:9 hero image of a cozy coffee shop at sunrise in our brand colors for the newsletter header.` - **Create an animated GIF** — from a prompt, image, or video. - Sample prompt: `Turn this product screenshot into a subtle looping GIF for my email: [image URL]` - **Export a finished design to your ESP** — push to Klaviyo, HubSpot, Mailchimp, and more as a reusable template. - Sample prompt: `Export my "Holiday Promo" design to Klaviyo as a template called "Holiday Promo 2026."` - **Start from the template gallery** — browse and remix on-brand templates. - Sample prompt: `Show me welcome-email templates in the gallery and remix the "Monk Welcome" one to match my brand.`

Nitrosend is an AI-native email platform that brings full-stack email automation directly into your AI agent of choice—Claude, ChatGPT, Codex, Cursor, or any MCP-compatible tool. Rather than an email tool with AI bolted on, Nitrosend is built AI-first, letting you compose, manage, and send transactional emails, marketing campaigns, and automation flows entirely through natural-language prompts with human approval gates. The MCP server exposes a comprehensive set of tools spanning the entire email lifecycle. These include account and brand management, audience and contact operations, segment definition, campaign and flow composition, reusable template management, and one-off transactional message sending. Emails are designed to be pixel-perfect and on-brand automatically, using each brand's own Brand Kit (colors, fonts, logo), domains, and audience context. Beyond content creation, the tools cover the operational backbone of email: sending domain setup and DNS verification, sender-default configuration, deliverability review and spam scoring, test sends, and full delivery lifecycle control (draft → approve → schedule/send → pause/archive). Additional tooling handles analytics and insights (via NitroWheel LLM), BYO email provider credentials (Mailgun, SES, Postmark, Resend, SendGrid), billing, AI memory management, and documentation search. Built by a team behind two prior email platforms that have sent over 6 billion emails, Nitrosend emphasizes enterprise-grade deliverability with separated transactional and marketing streams. ## Use Cases **1. Composing and sending on-brand marketing campaigns** Draft a full newsletter or promotional email from a single prompt, with the AI writing copy and generating a brand-matched design before you approve and send. - Sample prompt: `Draft a February newsletter campaign for our subscribed audience announcing our new AI segments feature, then show me a preview before sending.` **2. Building multi-step automation flows** Create event-triggered sequences like welcome series, cart abandonment, or password reset flows with waits, splits, and conditional branches. - Sample prompt: `Create a 3-email welcome flow triggered when a contact is added to the "New Signups" list, with a 1-day wait between each email.` **3. Sending transactional messages instantly** Fire off receipts, OTPs, order confirmations, or password resets to a single recipient using merge variables—no campaign or approval needed. - Sample prompt: `Send a transactional order confirmation email to jane@example.com using the order_confirmation template with order ID #10482 and total $89.00.` **4. Setting up and verifying sending domains** Add a sending domain, retrieve the required DNS records, and verify propagation to unlock sending. - Sample prompt: `Add send.acme.com as a sending domain and show me all the DNS records I need to configure, then check whether they've propagated.` **5. Managing audiences, lists, and subscriptions** Create or update contacts, manage list membership, record events, and handle opt-in/opt-out states. - Sample prompt: `Add contacts john@acme.com and sara@acme.com to the "VIP Customers" list and tag them both as "high-value".` **6. Defining targeted contact segments** Build dynamic segments with attribute and event-based filters, preview matching contacts, then persist them for campaign targeting. - Sample prompt: `Create a segment of email-subscribed contacts who performed "checkout" at least twice in the last 30 days and preview how many match.` **7. Importing contacts in bulk** Import contacts from inline records or a CSV upload, mapping custom fields and adding them to lists. - Sample prompt: `Import this CSV of 5,000 contacts and add them all to the "Q1 Prospects" list, mapping the "company" column to a custom field.` **8. Setting up a Brand Kit from a website** Auto-scrape brand colors, fonts, and logo from a URL so all generated emails match your brand automatically. - Sample prompt: `Set up our Brand Kit by scraping https://acme.com and set our corner radius to 4px with spacious section spacing.` **9. Reviewing deliverability and sending test messages** Check spam scores, validation, and preflight readiness, then send a real test to yourself before launching. - Sample prompt: `Review the deliverability of my latest campaign, check its spam score, then send a test to me@acme.com.` **10. Analyzing email performance** Pull analytics with trends, benchmarks, and recommendations at the account, campaign, or flow level. - Sample prompt: `Show me the open and click rates for all my campaigns over the last 90 days with recommendations to improve.` **11. Managing the delivery lifecycle** Approve, schedule, pause, or cancel campaigns and flows with explicit control over when messages go out. - Sample prompt: `Schedule my "Spring Sale" campaign to send on March 1st at 10am UTC.` **12. Bringing your own email provider** Connect your existing SendGrid, Postmark, Resend, Mailgun, or SES credentials for sending. - Sample prompt: `Configure my Postmark account as the sending provider using my API key and check that it's healthy.` **13. Managing multi-brand operations** Switch between brands and accounts to run email for multiple clients or products from one connection. - Sample prompt: `Switch to the "Obooko" brand and show me its current campaigns and readiness status.` **14. Searching product documentation** Look up authoritative setup, API, CLI, or integration instructions instead of guessing. - Sample prompt: `Search the Nitrosend docs for how to connect Cursor via MCP and walk me through the setup.` **15. Managing reusable email templates** Create, clone, or make targeted edits to reusable templates that apply your brand theme automatically. - Sample prompt: `Clone our "Product Update" template and change the header CTA button text from "Learn more" to "Get started".`

This MCP server connects AI assistants to **Uptime.com**, a leading website and API monitoring platform trusted by enterprises worldwide for uptime monitoring, performance tracking, and incident management. It provides comprehensive visibility across on-premise, cloud, and hybrid infrastructures through a globally distributed observability network of 80+ points of presence. The server exposes an extensive set of tools for creating and managing monitoring checks of nearly every type—including HTTP/HTTPS, DNS, SSL certificates, ICMP/Ping, TCP/UDP ports, email servers (SMTP, IMAP, POP), SSH, NTP, WHOIS/RDAP domain expiry, malware and blacklist scans, page speed (Google Lighthouse), Real User Monitoring, API and browser transaction checks, heartbeats, webhooks, and cloud service status tracking. Beyond check creation, the tools support full lifecycle operations: updating and deleting checks, managing contacts and notification groups, organizing checks with tags, and building custom dashboards. It also covers status page management (pages, components, and incidents) and rich observability queries for alerts, outages, historical statistics, probe locations, and account usage/limits. ## Use Cases **1. Website and API uptime monitoring setup** Quickly create HTTP, SSL, and DNS checks to ensure your key services are reachable and secure. - Sample prompt: `Create an HTTPS check for https://mystore.com that runs every 1 minute from US East and Europe locations, and alert my "DevOps" contact group if it goes down.` **2. SSL certificate and domain expiry protection** Avoid unexpected outages caused by expired certificates or lapsed domain registrations. - Sample prompt: `Set up an SSL check for example.com that alerts me 30 days before the certificate expires, plus a WHOIS check to warn me 45 days before the domain expires.` **3. Email server health monitoring** Verify that your mail infrastructure is fully operational across protocols. - Sample prompt: `Create SMTP, IMAP, and POP3 checks for mail.mycompany.com using STARTTLS encryption, checking every 5 minutes from the London probe location.` **4. Third-party cloud dependency tracking** Monitor the status of external providers your business relies on. - Sample prompt: `List available cloud status providers, then create a Cloud Status check that tracks AWS EC2 and alerts my "Platform" team when there's a disruption.` **5. Page speed and real user performance optimization** Track front-end performance to keep your site fast for real visitors. - Sample prompt: `Create a page speed check for https://mystore.com emulating a mobile device, and set up a Real User Monitoring check to measure actual visitor load times.` **6. Status page and incident communication** Keep customers informed about outages and scheduled maintenance. - Sample prompt: `Create a public status page called "MyApp Status", add components for API, Website, and Database, then post a scheduled maintenance incident for this Saturday 2-4am UTC.` **7. Heartbeat and cron job monitoring** Ensure background jobs and scheduled tasks are running as expected. - Sample prompt: `Create a heartbeat check named "Nightly Backup" that expects a ping every 24 hours and notifies the "Ops" contact group if the backup fails to run.` **8. Alert and outage investigation** Diagnose incidents and review historical reliability data. - Sample prompt: `Show me all unresolved alerts from the past 7 days, then pull the uptime statistics and outage history for my main website check.` **9. Dashboard creation for NOC visibility** Build consolidated views of monitoring data organized by service or team. - Sample prompt: `Create a pinned dashboard called "Production Overview" that shows all checks tagged "production", including down and paused services, with the 10 most recent alerts.` **10. Contact and notification management** Configure who gets alerted and how. - Sample prompt: `Create a new contact group called "On-Call Engineers" with emails alice@corp.com and bob@corp.com plus SMS to +14155550123.` **11. Multi-step transaction and API monitoring** Validate critical user flows and complex API sequences with browser and request automation. - Sample prompt: `Create a transaction check named "Checkout Flow" that runs every 10 minutes from three US locations and alerts the "Ecommerce" team on failure.` **12. Tag-based organization and capacity planning** Keep large monitoring setups organized and stay within plan limits. - Sample prompt: `Create a tag called "critical" in red, apply it to my payment gateway checks, then show my current account usage to see how many checks I have left.` **13. Security and reputation monitoring** Detect if your domain has been flagged for malware or blacklisted. - Sample prompt: `Set up a malware check and a domain blacklist check for mycompany.com, notifying my "Security" contact group of any issues.` **14. Infrastructure connectivity checks** Monitor low-level network services and ports. - Sample prompt: `Create an ICMP ping check and a TCP check on port 5432 for db.internal.example.com, both running every 2 minutes from my nearest probe location.`

This MCP server, provided by **Clarity AI**, exposes tools for **SFDR 2.0 (Sustainable Finance Disclosure Regulation) fund classification and assessment**. Clarity AI is an AI-native platform for extra-financial intelligence, serving financial institutions, companies, governments, and consumers with high-quality, traceable sustainability and regulatory data across millions of companies, funds, and sovereigns. The server centers on **securities discovery and SFDR 2.0 evaluation**. It allows users to search securities by keyword, ISIN, or CUSIP, and to run SFDR 2.0 category checks—either from a free-text fund name or an explicit ISIN—returning interpreted, evidence-based assessments. The toolset also includes a lower-level interpretation utility that normalizes raw SFDR 2.0 flags (Article 7, 8, 9, and mixed exclusion/contribution signals) into structured, category-level assessment evidence. Note that several tools are provided as deprecated backward-compatibility aliases, with the fund-name and ISIN-based tools being the preferred entry points. ## Use Cases **1. Search for a security or fund by name, ISIN, or CUSIP** Quickly locate a specific instrument to confirm identifiers before deeper analysis. - Sample prompt: `Search for the security with ISIN IE00B4L5Y983 and show me its details.` **2. Assess SFDR 2.0 classification from a fund name (natural language)** The preferred workflow for resolving free-text fund names and running an interpreted SFDR 2.0 category assessment. - Sample prompt: `Show the SFDR 2.0 category assessment evidence for the Nordea 1 Global Climate and Environment Fund.` **3. Run an SFDR 2.0 check using an explicit ISIN** When you already have a precise ISIN, get a direct SFDR 2.0 assessment for that instrument. - Sample prompt: `Run an SFDR 2.0 check on ISIN LU0348926287 using my email jane.doe@assetmanager.com.` **4. Compare SFDR 2.0 classifications across multiple funds** Combine fund-name assessments to benchmark several products for a due-diligence or product-shelf review. - Sample prompt: `Compare the SFDR 2.0 categories for the iShares MSCI World SRI ETF and the Amundi MSCI Europe ESG Leaders Fund.` **5. Interpret raw SFDR 2.0 flags into category-level evidence** Normalize a set of Article 7/8/9 and mixed exclusion/contribution flags into a clear, human-readable assessment. - Sample prompt: `Interpret these SFDR 2.0 flags: Article 8 contribution = 1, Article 8 exclusion = 1, all others = 0. What category does this indicate?` **6. Discover and then assess a fund in one workflow** Search for a partially remembered fund, then run the SFDR 2.0 assessment on the resolved match. - Sample prompt: `Find the S&P 500 tracker fund and then give me its SFDR 2.0 category assessment with supporting evidence.` **7. Regulatory compliance and reporting support** Generate defensible, traceable SFDR 2.0 evidence to support client disclosures and regulatory filings. - Sample prompt: `I need SFDR 2.0 classification evidence for the BlackRock Sustainable Energy Fund to include in our regulatory disclosure report.`

The **Postiz** MCP server provides a comprehensive suite of tools for automating social media management across 30+ networks, including X, LinkedIn, Instagram, Facebook, TikTok, YouTube, Reddit, Threads, Bluesky, Mastodon, and many more. Postiz is an open-source, AI-powered social media scheduling platform that lets users plan, generate, and schedule posts automatically, then review and edit everything in a visual calendar. The tools fall into several main categories: **account management** (listing integrations and customer groups), **content scheduling** (understanding platform schemas and scheduling posts with comments), and **AI media generation** (creating images and videos to attach to posts). There are also supporting utilities for uploading external media and triggering platform-specific data lookups. Together, these tools enable a fully agentic workflow — you can draft content, generate matching visuals, and schedule everything end-to-end across multiple platforms and client workspaces, all through natural conversation with the built-in "postiz" agent. ## Use Cases **1. Cross-posting content to multiple platforms** Write content once and schedule it simultaneously across several networks, with the schema tool ensuring each platform's requirements are met. - Sample prompt: `Schedule a post on LinkedIn, X, and Threads announcing our new product launch this Thursday at 10am UTC, with the text "Excited to introduce our latest feature!"` **2. Scheduling posts with comments/threads** Create a main post plus follow-up comments (useful for threads or adding links in the first comment). - Sample prompt: `Post a tweet on X saying "Big news coming tomorrow 🚀" and add a first comment with the link to our blog at example.com/blog` **3. Generating AI images for posts** Create visuals from a text prompt and attach them directly to a scheduled post when no media is provided. - Sample prompt: `Generate an image of a golden retriever wearing sunglasses on a beach and attach it to an Instagram post scheduled for Saturday morning` **4. Generating AI videos (UGC / Veo3 / slides)** Produce short AI-generated video clips (image-text slides or audio+video) for platforms that require video attachments. - Sample prompt: `Create a vertical Veo3 video promoting our coffee brand and schedule it as a TikTok post for next Monday at 6pm` **5. Managing multiple brands/clients (customer groups)** List customer groups and filter integrations belonging to a specific brand, ideal for agencies handling several clients. - Sample prompt: `Show me all the connected social accounts for my client "Acme Corp" and schedule a Facebook post for them` **6. Uploading external media before scheduling** Import an image or video from a public URL into the media library so it passes upload validation before being attached. - Sample prompt: `Take this image URL https://example.com/photo.jpg and use it as the attachment for a Pinterest post scheduled tomorrow` **7. Bulk scheduling across days** Schedule many individual posts spread across different days in a single request. - Sample prompt: `Schedule 7 different motivational quote posts on X, one each day for the next week starting Monday at 9am UTC` **8. Saving posts as drafts for review** Create draft posts that can be reviewed and edited in the visual calendar before publishing. - Sample prompt: `Draft a LinkedIn post about the benefits of remote work so I can review it before it goes live` **9. Conversational end-to-end scheduling via the agent** Delegate an entire multi-step workflow to the built-in "postiz" agent that drafts copy, generates media, and schedules it. - Sample prompt: `Ask the postiz agent to write a LinkedIn post about AI productivity tools, generate a matching image, and schedule it for Tuesday at 9am` **10. Discovering available accounts before posting** List all available integrations to confirm which channels are connected and pick the right one. - Sample prompt: `List all my connected social media accounts so I can decide where to post my announcement`

This MCP server connects to **Tonic Fabricate**, Tonic.ai's synthetic data platform for generating safe, realistic data on demand. Fabricate lets engineering and AI teams create fully relational synthetic databases—either from scratch or modeled on existing production patterns—to unblock app development, testing/QA, AI model training, and reinforcement learning workflows without exposing sensitive data. The core of the server is an **AI data-generation agent** that runs asynchronously. You start a conversation with a natural-language instruction, the agent works in the background (minutes to hours) building databases, and you poll for status and fetch results. Tools support the full conversation lifecycle: starting, sending follow-ups, checking status, retrieving results, stopping, listing, and reverting to earlier states. Beyond conversations, the server provides **workspace and project management** (create, list, and delete workspaces and projects), **file handling** (direct uploads to seed SQLite databases or attach files, plus downloading generated artifacts), and **reusable workflows** (project-scoped scripts you can run with parameters). It also includes robust **account governance and observability** for owners—AI usage costs, seat/member usage, API key activity, audit trails, and per-surface API/MCP call counts. ## Use Cases ### 1. Generate a synthetic database from scratch Spin up a fully relational, realistic dataset for a new product or demo without any production data. - Sample prompt: `Create a synthetic Postgres-style database for an urgent care clinic with tables for patients, visits, treatments, and clinician visit notes—about 500 patients and realistic referential integrity.` ### 2. Seed and model from an existing database Upload an existing SQLite database and have the agent model its structure and distributions. - Sample prompt: `I've uploaded my seed.db file—model its schema and data distributions, then generate 10,000 new synthetic customer and order records that match those patterns.` ### 3. Track a long-running generation job Start a job and monitor it asynchronously to completion. - Sample prompt: `Start generating a large e-commerce dataset with products, inventory, orders, and reviews, then keep checking the status and let me know when it's done and give me the download links.` ### 4. Build evaluation / fine-tuning datasets for AI Generate labeled data with ground truth for model training or agent testing. - Sample prompt: `Generate a labeled dataset of 2,000 customer support tickets with categories, sentiment scores, and resolution notes that I can use to fine-tune a classification model.` ### 5. Iteratively refine generated data Continue a conversation to adjust or expand what the agent produced. - Sample prompt: `In conversation abc123, add a fraud_flags table and make about 3% of the transactions look anomalous with unusual amounts and timing.` ### 6. Revert a conversation to an earlier state Undo changes by rolling databases back to before a specific message. - Sample prompt: `Revert the loan-portfolio conversation back to before the message where I asked you to add the derivatives tables—that direction didn't work out.` ### 7. Organize work across workspaces and projects Manage where generated data lives. - Sample prompt: `Create a workspace called "QA Sandbox", then list all projects in it sorted by most recently created.` ### 8. Download generated artifacts and exports Retrieve the produced databases or exported files. - Sample prompt: `List all the files my "healthcare demo" conversation produced and give me a direct download link for the exported CSV.` ### 9. Create and run reusable workflows Execute parameterized, project-scoped scripts for repeatable data tasks. - Sample prompt: `List the workflows in my analytics project, then run the "monthly_refresh" workflow with region set to EMEA and record_count set to 5000.` ### 10. Configure generation models and validation Inspect available models and reasoning tiers before launching a run. - Sample prompt: `Show me the available models and reasoning-effort tiers, then start a high-effort generation with both code review and data review validation enabled.` ### 11. Monitor AI usage costs Track token spend and usage across scopes. - Sample prompt: `Show me the total AI usage cost and token breakdown for my "fintech-sim" project since January 1st, 2026.` ### 12. Account governance and seat management (owners) Review how account seats are being used and by whom. - Sample prompt: `As the account owner, summarize our seat usage—how many seats are used versus available, and show each member's last active date and AI spend this month.` ### 13. Audit trail and compliance review (owners) Trace exactly what users and API keys did. - Sample prompt: `Show me all database_export and database_download audit events from the last 30 days, and tell me which user or API key performed each one.` ### 14. Measure programmatic API/MCP usage (owners) Break down call volume by caller and surface. - Sample prompt: `How many API and MCP calls did each user and API key make last quarter? Break it down by surface and show the heaviest callers first.`

Tavily provides a web access layer built specifically for AI agents and applications, offering one secure API for real-time web access. Trusted by over 2 million developers and used by enterprises like MongoDB, IBM, AWS, and JetBrains, Tavily grounds language models with fresh, structured web context to help agents reason over facts without hallucinating. This MCP server exposes Tavily's core capabilities through five complementary tools: **search** for retrieving current web information, **extract** for pulling clean content from specific URLs, **crawl** for exploring websites with configurable depth and breadth, **map** for discovering a website's structure, and **research** for conducting comprehensive multi-source investigations. Together, these tools enable AI agents to fetch live data, parse and structure web content, and perform deep research—all backed by a production-grade retrieval stack featuring built-in safeguards against PII leakage, prompt injection, and malicious sources. With 300M+ monthly requests handled and 99.99% uptime, Tavily is engineered for reliability at scale. ## Use Cases **1. Real-time factual search and news lookup** Use `tavily_search` to retrieve up-to-date information on any topic that falls beyond a model's knowledge cutoff, with control over recency, domains, and geographic relevance. - Sample prompt: `Search for the latest news on OpenAI's product announcements from the past week and give me the source URLs.` **2. Extracting content from specific web pages** Use `tavily_extract` to pull clean, structured markdown or text content from one or more known URLs, including protected sites, LinkedIn, or pages with tables. - Sample prompt: `Extract the full article content from these three URLs and summarize the key points from each.` **3. Crawling a website for documentation or research** Use `tavily_crawl` to systematically explore a site starting from a root URL, gathering content across multiple pages with defined depth and path filters. - Sample prompt: `Crawl the docs.stripe.com site and collect all pages under the /payments path, then summarize their API authentication requirements.` **4. Mapping a website's structure** Use `tavily_map` to generate a list of all URLs reachable from a base URL, useful for site audits or planning a targeted crawl. - Sample prompt: `Map the entire structure of https://www.example.com and show me all the pages under the /blog section.` **5. Comprehensive multi-source research** Use `tavily_research` to conduct deep investigations across many sources and receive a detailed synthesized response, ideal for broad or complex questions. - Sample prompt: `Research the current state of the electric vehicle battery market, including major manufacturers, recent breakthroughs, and projected growth through 2030.` **6. Competitive and market intelligence (combined tools)** Combine `tavily_search` and `tavily_extract` to first discover relevant competitor pages, then extract detailed content for analysis. - Sample prompt: `Find the pricing pages for the top 3 project management SaaS tools, extract their pricing tiers, and build a comparison table.` **7. Country-specific and time-bound fact checking** Use `tavily_search` with country and date-range filters to verify claims or gather region-specific data within a defined time window. - Sample prompt: `Search for renewable energy policy changes in Germany between 2024-01-01 and 2024-12-31 and list the key legislative updates.` **8. Building a knowledge base from a domain (combined tools)** Combine `tavily_map` and `tavily_crawl` to first map a site's structure, then crawl selected sections to ingest content into a knowledge base. - Sample prompt: `Map the help center at support.myproduct.com, then crawl all the troubleshooting articles so I can build an internal FAQ dataset.`

The **Microsoft Learn MCP Server** provides AI-powered access to official Microsoft and Azure documentation, enabling assistants to ground their answers in accurate, first-party knowledge. It connects directly to Microsoft Learn and other trusted Microsoft sources, ensuring responses are always based on the latest official content. The server offers three complementary tools focused on documentation retrieval and code assistance. These include searching documentation for relevant content chunks, fetching complete webpages in markdown format, and retrieving official code samples for Microsoft/Azure development tasks. Together, these tools help developers, IT professionals, and learners get trustworthy answers, step-by-step guidance, and practical code examples across the entire Microsoft ecosystem—including Azure, .NET, Microsoft 365, and Copilot technologies. ## Use Cases ### 1. Grounding answers in official Microsoft documentation Quickly search across Microsoft Learn to find relevant, accurate content for any Microsoft/Azure question, ensuring responses reflect first-party knowledge. - Sample prompt: `How do I configure managed identities for an Azure App Service to access Azure Key Vault?` ### 2. Retrieving complete tutorials and step-by-step guides When a search result is truncated or highly relevant, fetch the full documentation page to get comprehensive procedures, prerequisites, and troubleshooting details. - Sample prompt: `Give me the complete step-by-step tutorial for deploying a containerized app to Azure Kubernetes Service, including all prerequisites.` ### 3. Generating code with the latest official samples Retrieve up-to-date, official code snippets when writing Microsoft/Azure related code, filtered by your preferred programming language. - Sample prompt: `Show me a C# code sample for uploading a file to Azure Blob Storage using the latest Azure.Storage.Blobs SDK.` ### 4. Comparing services and making architecture decisions Search documentation to understand differences between Microsoft services and get authoritative guidance for architectural choices. - Sample prompt: `What's the difference between Azure Functions and Azure Logic Apps, and when should I use each based on official Microsoft guidance?` ### 5. Troubleshooting errors with authoritative sources Search and fetch detailed troubleshooting sections from Microsoft docs to resolve specific errors or configuration issues. - Sample prompt: `I'm getting a "403 Forbidden" error when calling the Microsoft Graph API. Find the official troubleshooting steps to fix this.` ### 6. Learning SDK and API usage patterns Combine documentation search with code sample retrieval to fully understand how to use a specific SDK, class, or method. - Sample prompt: `Explain how to use the Azure OpenAI Python SDK to create chat completions, and include a working code example.` ### 7. PowerShell and Azure CLI automation scripting Retrieve official command-line samples for automating Azure resource management and administrative tasks. - Sample prompt: `Give me a PowerShell script to create a new Azure resource group and deploy a virtual machine into it using official examples.` ### 8. Exploring new Microsoft features and best practices Search the latest documentation to stay current with new capabilities and recommended best practices across Microsoft products. - Sample prompt: `What are the current best practices for securing a .NET 8 web API with Microsoft Entra ID authentication?`

The GitHub MCP Server is GitHub's official Model Context Protocol server, designed to connect AI assistants and agents directly to GitHub's platform. It exposes a comprehensive set of tools that allow language models to interact with repositories, issues, pull requests, files, branches, releases, and teams programmatically. This server enables end-to-end software development workflows, from creating and managing repositories to handling code reviews and collaboration. The available tools cover repository management (creating repos, branches, and files), issue tracking (creating, updating, and commenting on issues), pull request review workflows (adding review comments, replies, and reactions), and organizational context (retrieving teams, members, and authenticated user details). Maintained by GitHub and released under the MIT license, the server supports read-only and lockdown modes for security-conscious environments and is highly configurable. It serves as a powerful bridge for automating and streamlining common developer operations through natural language. ## Use Cases ### 1. Repository Setup and Management Quickly bootstrap new projects by creating repositories, initializing files, and setting up branches without leaving your AI assistant. - Sample prompt: `Create a new private repository called "payment-service" in my organization "acme-corp", initialize it with a README, and add a description saying "Handles payment processing".` ### 2. File Operations and Content Management Create, update, retrieve, or delete files directly in a repository, useful for configuration changes or quick edits. - Sample prompt: `Update the file "config/settings.yaml" on the "develop" branch of acme-corp/payment-service to change the timeout value to 30 seconds, and commit it with the message "Increase timeout".` ### 3. Issue Tracking and Triage Create, update, and organize issues, including assigning labels, milestones, assignees, and managing sub-issue hierarchies. - Sample prompt: `Create a new issue in acme-corp/payment-service titled "Fix null pointer in checkout flow", assign it to user "janedoe", add the labels "bug" and "priority-high", and set the body describing the crash on the payment confirmation page.` ### 4. Pull Request Creation and Review Open pull requests, request reviewers, and add detailed line-by-line review comments on the diff to facilitate code review workflows. - Sample prompt: `Create a pull request in acme-corp/payment-service from branch "feature/retry-logic" into "main" titled "Add retry logic for failed payments", and request review from "senior-dev-team".` ### 5. Code Review Comments and Feedback Add pending review comments, reply to existing comments, and react to feedback on specific lines of code. - Sample prompt: `On pull request #42 in acme-corp/payment-service, add a review comment on line 85 of "src/checkout.js" on the RIGHT side saying "This should handle the timeout exception explicitly".` ### 6. Issue and PR Collaboration Add comments and emoji reactions to issues and pull requests to keep discussions moving and acknowledge contributors. - Sample prompt: `Add a comment to issue #17 in acme-corp/payment-service saying "I've reproduced this bug and will have a fix by tomorrow", and add a rocket reaction to the issue itself.` ### 7. Release and Tag Inspection Retrieve the latest releases, specific releases by tag, or git tag details to track versioning and deployment history. - Sample prompt: `Get the latest release for acme-corp/payment-service and show me its release notes and tag name.` ### 8. Commit and Change Investigation Fetch detailed commit information, including file-level stats or full diffs, to understand changes and troubleshoot regressions. - Sample prompt: `Show me the full patch details for commit abc123def in acme-corp/payment-service so I can see exactly what changed in the checkout module.` ### 9. Repository Forking and Branching for Contribution Fork repositories and create feature branches to contribute to open-source or team projects. - Sample prompt: `Fork the repository octocat/hello-world to my account, then create a new branch called "fix-typo" from the main branch.` ### 10. Team and Organization Context Retrieve team memberships and member lists to understand organizational structure and route reviews or assignments appropriately. - Sample prompt: `List all the teams I'm a member of, and then show me the members of the "backend-engineers" team in the "acme-corp" organization.` ### 11. Label Management and Standardization Fetch label details to keep issue categorization consistent across a repository. - Sample prompt: `Get the details of the "priority-high" label in acme-corp/payment-service including its color and description.` ### 12. Authenticated User Profile Lookup Retrieve details of the authenticated user to personalize actions or auto-fill ownership information in other operations. - Sample prompt: `Who am I logged in as? Get my GitHub profile details and list the repositories I own.`

The **Hugging Face MCP Server** connects AI assistants to Hugging Face's vast ecosystem of machine learning resources, including models, datasets, spaces (AI applications), research papers, and documentation. As an open-source offering from Hugging Face, this server provides a secure, standardized bridge between conversational AI clients and the Hub's rich collection of state-of-the-art AI tools and content. The server exposes tools for authenticated identity checks, powerful repository search, detailed repository inspection, and a flexible filesystem-style navigation interface. Together, these tools let assistants discover trending models, preview datasets, explore AI spaces, and read research papers—all through simple, structured queries. Whether you're browsing the latest text-generation models, inspecting a dataset's schema before use, or reading through a paper's content, the Hugging Face MCP Server makes the entire Hub programmatically accessible to your AI workflows. ## Use Cases **1. Discovering Trending and Relevant Models** Search across models, datasets, and spaces with filtering by author, tags, popularity, and recency to find exactly what you need. - Sample prompt: `Find me the top 10 most downloaded text-generation models from Meta on Hugging Face, and include links to each repository.` **2. Exploring and Previewing Datasets** Inspect dataset structure to discover configs, splits, and schema, then preview actual rows before committing to a download or fine-tuning task. - Sample prompt: `Show me the structure of the 'squad' dataset, then preview the first 5 rows of the training split.` **3. Getting Detailed Model Information** Retrieve overviews for one or more repositories to compare capabilities, licenses, and specifications side by side. - Sample prompt: `Give me the details and overview for openai/gpt-oss-120b and meta-llama/Llama-3-70B so I can compare them.` **4. Finding AI Applications (Spaces)** Use semantic search and tag filtering to locate ready-to-use AI apps and demos, including MCP-server spaces. - Sample prompt: `Search Hugging Face Spaces for text-to-image generation apps and show me the most trending ones.` **5. Reading Research Papers** Navigate to papers by arXiv ID to discover related resources and read the full paper content or metadata directly. - Sample prompt: `Pull up the paper with arXiv ID 1706.03762 and summarize its key contributions for me.` **6. Browsing Trending Content** List trending models, datasets, spaces, or papers to stay current with the latest developments in the AI community. - Sample prompt: `What are the top trending datasets on Hugging Face right now?` **7. Navigating Repository Files** Use filesystem-style commands (ls, cat, stat, find) over hf:// URIs to explore repository contents, read config files, or locate specific files. - Sample prompt: `List all the files in the mistralai/Mistral-7B-v0.1 model repository and show me the contents of its config.json.` **8. Searching Documentation** Query Hugging Face's product documentation to find guidance on transformers, datasets, or other libraries. - Sample prompt: `Search the Hugging Face Transformers documentation for how to use the Trainer API.` **9. Author-Scoped Discovery** Filter searches to a specific organization or user to survey their entire catalog of contributions. - Sample prompt: `Show me all the datasets published by the 'google' organization on Hugging Face, sorted by likes.` **10. Verifying Authentication Context** Confirm the identity of the authenticated user driving Hub interactions. - Sample prompt: `Which Hugging Face account am I currently authenticated as?`

This MCP server connects to **Teamwork.com**, an AI-powered professional services automation platform that helps agencies, consultancies, IT service providers, and other client-focused businesses run projects, resources, and financials in one place. The server exposes a comprehensive set of tools spanning the entire Teamwork.com ecosystem, enabling AI assistants to manage work end-to-end without leaving the conversation. The tools are organized into four main product areas. **Teamwork Projects** (`twprojects-*`) covers the core project management surface — projects, tasks, tasklists, milestones, comments, messages, notebooks, links, time tracking (timelogs and timers), workflows, custom fields, custom item types, budgets, teams, users, tags, skills, and workload reporting. **Teamwork Desk** (`twdesk-*`) handles customer support with tickets, inboxes, customers, companies, priorities, statuses, ticket types, help doc articles, and agents. **Teamwork Chat** (`twchat-*`) provides messaging capabilities including conversations, direct messages, and people lookup, while **Teamwork Spaces** (`twspaces-*`) manages a knowledge base of collaborative pages, comments, categories, tags, and spaces. Together these tools let you create, read, update, search, and report across the full Teamwork.com suite — from logging billable time to spinning up projects from templates and forecasting team workload. ## Use Cases ### 1. Project setup and cloning Quickly stand up new client projects manually or from templates, then organize tasklists and milestones. - Sample prompt: `Create a new project called "Acme Website Redesign" for the Acme company starting 2025-07-01, then add tasklists for Discovery, Design, and Development.` - Sample prompt: `Clone our "Client Onboarding Template" project into a new project called "Beta Corp Onboarding" starting today.` ### 2. Task management and delegation Create, assign, prioritize, and track tasks through completion and workflow stages. - Sample prompt: `Create a high-priority task "Finalize homepage mockups" in tasklist 48291, assign it to user 1023, due next Friday, and estimate 4 hours.` - Sample prompt: `Show me all unassigned, unplanned tasks in project 55210 and list who created each one.` ### 3. Time tracking and billable reporting Log time, run timers, and produce exact profitability-focused totals grouped by user or project. - Sample prompt: `Log 2 hours 30 minutes of billable time against task 88213 for today with the description "Client review call".` - Sample prompt: `Summarize all billable vs unbilled hours per project for the month of June 2025.` ### 4. Resource and workload planning See task allocation across the team to spot over- and under-utilization. - Sample prompt: `Show me the workload for the design team between 2025-07-01 and 2025-07-15 and flag anyone over capacity.` ### 5. Customer support ticket handling (Teamwork Desk) Triage, reply to, and update support tickets, and manage inboxes, statuses, and priorities. - Sample prompt: `Find all open high-priority tickets in the Support inbox, then reply to ticket 41022 letting the customer know a fix is on the way.` - Sample prompt: `Add an internal note to ticket 41055 assigning it to agent 88 and tag it as "escalation".` ### 6. Customer and company (client) records management Create and maintain customer and company records across both Projects and Desk. - Sample prompt: `Create a new Desk company called "Northwind Traders" with the domain northwind.com and add a customer named Jane Doe with email jane@northwind.com.` ### 7. Cross-entity and knowledge search Search across projects, tasks, files, messages, help docs, and Spaces pages. - Sample prompt: `Search all projects and tasks for anything mentioning "GDPR compliance" updated in the last month.` - Sample prompt: `Search our help docs for published articles about "password reset".` ### 8. Team communication (Teamwork Chat) Send direct messages and post to conversations, resolving people by name. - Sample prompt: `Send a direct message to Sarah Kelly letting her know the design review is moved to 3pm tomorrow.` - Sample prompt: `Post a message in the "Project Leads" conversation summarizing this week's blockers.` ### 9. Knowledge base authoring (Teamwork Spaces) Create and maintain internal documentation pages, comments, and required reading. - Sample prompt: `Create a page titled "Client Onboarding Checklist" in space 12 and publish it with our standard onboarding steps.` - Sample prompt: `Duplicate page 8842 as "2025 Style Guide" and mark it as required reading.` ### 10. Custom items for bespoke workflows Model non-standard entities like Contracts, Leads, or Deals with custom fields and records. - Sample prompt: `List the custom item types on project 55210, then add a new "Contract" record named "Acme Retainer 2025" with a value of 24000 and status Active.` ### 11. Custom fields and metadata Define and populate custom fields on tasks, projects, and companies for richer reporting. - Sample prompt: `Create a dropdown custom field called "Account Tier" with Gold, Silver, and Bronze options at the installation level, then set it to Gold on project 55210.` ### 12. Messages, notebooks, and links collaboration Centralize project discussions, reference docs, and shared resources. - Sample prompt: `Post a project message titled "Sprint 3 Kickoff" to project 55210 notifying all members, and add a link to our shared Figma board.` ### 13. Activity monitoring and status reporting Review recent activity and comments to build status updates. - Sample prompt: `Show me all activity in project 55210 over the last 7 days involving milestones and timelogs, then draft a client status summary.` ### 14. Workflow management Set up workflows and move tasks through stages for visual pipeline tracking. - Sample prompt: `Create a workflow called "Content Pipeline" with stages Draft, Review, and Published, link it to project 55210, and move task 88213 into Review.`

Hive Intelligence provides a managed MCP server that acts as a single connection for evidence-backed crypto due diligence. Every answer is delivered with sources, freshness context, and a runtime receipt (provider evidence time, cache age, runtime status, and a server-issued receipt ID), so agents can preserve verifiable provenance for each data point. Rather than exposing hundreds of endpoints directly, Hive uses a discover-then-execute workflow. Agents first search a catalog of canonical task toolsets and intent-based routes with `search_tools`, inspect the exact input contract with `get_api_endpoint_schema`, then run read-only queries through `invoke_api_endpoint` or approved state changes through `invoke_stateful_endpoint`. A final `validate_task_result` step checks the proposed answer against Hive's typed task-output and evidence-receipt contract. The underlying catalog spans market data, onchain activity, risk screening, and prediction markets, normalizing providers such as CoinGecko, Moralis, Tenderly, Helius, GoPlus, CCXT, Codex, Alchemy, DeFiLlama, and Hyperliquid. Its source-backed inventory covers roughly 10 category endpoints, 519 provider tools, and 18 Hive-native stateful tools, all reachable through the same bounded execution contract. ## Use Cases - **Token safety screening before signing/swapping** Combine risk tools like honeypot detection, contract risk, and rugpull signals to vet a token before any transaction. Sample prompt: `` Use Hive to screen the token at 0x1234...abcd on Ethereum for honeypot risk, contract vulnerabilities, and rugpull signals, and tell me if it's safe to research further. `` - **Live market price and stats lookups** Fetch current prices, volumes, market caps, movers, and global market stats across venues. Sample prompt: `Get me the current BTC and ETH prices in USD along with 24h volume and market cap using Hive tools.` - **Wallet portfolio and transaction history analysis** Pull token balances, transaction history, and DeFi positions for a given wallet. Sample prompt: `` Show me all token balances and the recent transaction history for wallet 0xabc...789 on Ethereum using Hive. `` - **DeFi protocol and TVL research** Inspect protocol TVL, DeFi positions, and trending liquidity pools for diligence reports. Sample prompt: `Use Hive to give me the current TVL for Aave and Uniswap, plus the top trending DEX pools right now.` - **Prediction market tracking and reporting** Discover prediction markets, monitor price movement, and aggregate trade flow for ranking or reporting. Sample prompt: `Track the top prediction markets on Codex through Hive and summarize recent trade flow and event stats for the biggest ones.` - **End-to-end due-diligence workflow with verifiable receipts** Run a full route (discover → inspect schema → execute → validate) that produces a pass/block/escalate decision backed by evidence receipts. Sample prompt: `` Run a full Hive due-diligence workflow on the PEPE token: check price, liquidity, and security risk, then give me a pass/block/escalate decision with runtime receipts for each source. `` - **DEX flow and trending pool discovery** Surface trending pools and DEX flows to spot emerging tokens or liquidity shifts. Sample prompt: `Use Hive to find the trending DEX pools on Solana in the last 24 hours and flag any with unusual liquidity changes.` - **Comparative market movers analysis** Query market data to identify top gainers and losers for agent-driven watchlists. Sample prompt: `Get the top 10 crypto gainers and losers over the last 24 hours from Hive market data with their current prices.` - **Saving and managing durable diligence state** Use Hive-native stateful endpoints (after explicit approval) to remember, acknowledge, or resolve monitoring alerts and reports. Sample prompt: `After reviewing this token risk report, save it to my Hive workspace and set an alert I can acknowledge later.` - **Validating an answer before presenting it to a user** Ensure a generated crypto answer meets citation, phase coverage, and receipt-consistency requirements before delivery. Sample prompt: `Validate my proposed token-risk answer against Hive's task-output contract and confirm each claim is backed by a proper evidence receipt.`

# Introduction Ref is a documentation context provider built by ref.tools that connects AI coding agents to accurate, up-to-date technical documentation through the Model Context Protocol (MCP). The company's mission is to reduce hallucinations in AI-generated code by grounding agents in real documentation rather than outdated or invented knowledge. The MCP server exposes two complementary tools focused on documentation retrieval. `ref_search_documentation` searches across public and private documentation sources—including web docs, GitHub repos, and PDFs—while `ref_read_url` reads the full content of any specific URL as clean markdown. Together, these tools enable a search-then-read workflow: agents first find relevant documentation sections, then dive deep into specific pages for complete context. This helps developers get precise, current answers about frameworks, APIs, libraries, and their own private codebases without context bloat. # Use Cases **1. Finding framework-specific implementation guidance** Search public documentation to get accurate syntax and best practices for a specific library or framework. - Sample prompt: `Search the React documentation for how to use the useEffect hook with cleanup functions and show me the correct pattern.` **2. Reading a specific documentation page in depth** After locating a relevant doc, read its full content to extract detailed information and code examples. - Sample prompt: `Read the full content of the Next.js App Router documentation page on server actions and summarize the setup steps for me.` **3. Searching private repos and internal docs** Query a team's private documentation, GitHub repos, or PDFs to ground agent work in existing conventions. - Sample prompt: `Search our private docs (ref_src=private) for our internal authentication service setup and how to configure the OAuth PKCE flow.` **4. Resolving API integration questions** Look up specific API endpoints, parameters, and authentication requirements from official service documentation. - Sample prompt: `Search the Stripe API documentation for how to create a subscription with a trial period, then read the exact endpoint reference.` **5. Debugging with accurate, current documentation** Prevent hallucinated fixes by pulling the latest official docs when troubleshooting an error. - Sample prompt: `I'm getting a "hydration mismatch" error in my Next.js 14 app. Search the docs for the cause and read the recommended solution.` **6. Comparing configuration options across versions** Search for version-specific documentation to ensure code matches the exact library version in use. - Sample prompt: `Search the Tailwind CSS v4 documentation for the new configuration format and show me how it differs from v3.` **7. Following documentation links for deeper context** Chain a search result into a full-page read to access content not surfaced in the initial search snippet. - Sample prompt: `Search the PostgreSQL docs for JSONB indexing, then read the full page on GIN indexes to explain performance tradeoffs.` **8. Onboarding to an unfamiliar codebase or tool** Search private repos and PDFs to quickly understand an internal library's usage patterns. - Sample prompt: `Search our private GitHub repo docs for how to use our internal logging SDK and read the getting-started guide.`

The Exa MCP server provides AI agents with powerful web search and content extraction capabilities, built specifically for AI-driven workflows. Developed by Exa (exa.ai), this server offers one unified API for search, crawling, and research, trusted by teams at Cursor, HubSpot, Databricks, and Cognition. The server exposes two complementary tools: `web_search_exa` for finding current information across any topic using semantically rich natural language queries, and `web_fetch_exa` for reading full webpage content as clean markdown. Together, these tools enable agents to retrieve token-efficient, ready-to-use content with clean text extraction from top search results. Exa is designed to make agents smarter by providing comprehensive coverage across multiple search verticals—including general web, company, people (LinkedIn profiles), and code searches. With features like highlights for token reduction and specialized category filtering, it's ideal for grounding agent responses in accurate, current, and cited information. ## Use Cases 1. **Current Events & News Research** Agents can search for the latest developments on any topic and pull the most relevant, up-to-date information. - Sample prompt: `Find the latest news about Nvidia's newest GPU releases and summarize the key announcements from the past month.` 2. **Company & Market Research** Using the `category:company` filter, agents can research businesses, competitors, and market landscapes. - Sample prompt: `Search for European Series B fintech companies with 50+ employees that raised funding recently, and give me details on each.` 3. **People & Talent Sourcing** The `category:people` filter allows searching LinkedIn-style profiles for recruiting or networking purposes. - Sample prompt: `Find profiles of senior machine learning engineers based in San Francisco who have experience at large AI labs.` 4. **Deep Content Extraction from Specific URLs** After finding relevant results, agents can fetch full page content as clean markdown for detailed analysis. - Sample prompt: `Read the full content of this article at https://example.com/ai-report-2025 and give me a detailed breakdown of the main findings.` 5. **Fact-Checking & Answering Questions** Agents can verify claims or answer factual queries with grounded citations from top sources. - Sample prompt: `Who is the current CEO of Boeing and when was the company founded? Provide sources.` 6. **Technical Documentation & Code Research** Search across docs and repositories for token-efficient answers to programming questions. - Sample prompt: `Find documentation and code examples comparing React and Vue performance for large-scale applications.` 7. **Multi-Source Content Aggregation** Combine both tools to search broadly then batch-fetch multiple URLs for comprehensive research. - Sample prompt: `Search for the top 5 articles comparing electric vehicle battery technologies, then read all of them and create a comparison table.` 8. **Competitive Intelligence & Enrichment** Gather structured data across companies for lead generation and market analysis. - Sample prompt: `Find the top aerospace companies, then read their about pages to extract each company's CEO name and founding year.`

The **HeyOnCall** MCP server provides a direct bridge between your AI assistant and HeyOnCall's incident alerting system. HeyOnCall is an all-in-one on-call platform that integrates on-call scheduling, alerting, website monitoring, and heartbeat monitoring into a single reliable product, serving DevOps, SRE, and engineering teams as well as solo developers. This server exposes a focused capability: triggering critical, attention-demanding alerts that page the on-call user. Its Critical Alerts bypass Do-Not-Disturb and volume settings, are loud enough to wake someone up, and repeat until acknowledged, ensuring urgent issues are never missed. With this MCP tool, an AI agent can autonomously escalate problems it detects—such as failed operations, service outages, or anomalies—by paging the responsible human with a concise message and relevant context. ## Use Cases - **Escalate a detected outage or failure to a human.** When an AI agent monitoring your infrastructure or logs discovers a critical problem it cannot resolve, it can page the on-call engineer immediately. - Sample prompt: `I just checked our production API and it's returning 500 errors on every request. Page the on-call engineer with a message that the checkout API is down and include the dashboard URL https://status.example.com/api.` - **Alert on a failed automated job or workflow.** If a scheduled task, deployment, or data pipeline run by the agent fails, it can trigger a page so someone can intervene. - Sample prompt: `The nightly database backup job failed with a disk-full error. Alert the on-call person that the Postgres backup did not complete tonight.` - **Manual paging during an incident.** A developer working with the assistant can ask it to page the on-call team directly when they've confirmed something is broken. - Sample prompt: `We're seeing checkout failures spike on the dashboard. Page on-call now with the message "Checkout error rate above 40%, needs immediate investigation" and link https://grafana.example.com/checkout.` - **Escalate security or anomaly findings.** After an agent flags a suspicious event or threshold breach, it can raise a critical alert to ensure a human reviews it right away. - Sample prompt: `Our SSL certificate for app.example.com expires in less than 24 hours and auto-renewal hasn't kicked in. Page the on-call engineer urgently about the expiring certificate.` - **Trigger a wake-up alert for time-sensitive issues.** For problems that genuinely can't wait, use HeyOnCall's Critical Alerts to bypass Do-Not-Disturb and reach the on-call person even overnight. - Sample prompt: `The primary payment processor is unreachable and transactions are queuing up. Send a critical page to whoever is on-call so they wake up and fix this immediately.` - **Notify on-call about degraded performance.** When latency or health metrics cross a concerning threshold, the agent can page rather than let the alert go unnoticed. - Sample prompt: `The API latency has jumped from 90ms to over 8 seconds for the last 10 minutes. Page the on-call engineer with a message about severe API latency degradation.`

The **Cleanup Crew** MCP server bridges the gap between AI coding assistants and human expertise. When AI tools like Cursor, GitHub Copilot, or Claude Code hit a wall on a complex problem, this server allows you to escalate the issue directly to a team of expert developers without leaving your IDE. The server exposes a single, powerful tool—`request_help`—that captures comprehensive context about your current coding session and packages it for the support team. This includes your conversation history, workspace state, file diffs, diagnostics, dependencies, and environment details, all sent seamlessly through the Model Context Protocol. Once a request is submitted, Cleanup Crew creates a private Discord channel and connects you with an expert developer within 15–60 minutes (depending on your subscription tier), offering real-time text and voice support until your issue is resolved. This makes it ideal for founders and developers who want human backup on-demand without hiring a full-time engineer. ## Use Cases **1. Debugging runtime errors AI can't diagnose** When your AI assistant repeatedly fails to fix a complex exception or stack trace, escalate the full error context to a human expert. - Sample prompt: `I've been stuck on this "Cannot read property 'map' of undefined" error in my React component for an hour and Claude keeps giving me fixes that don't work. Request help from the support team with my current error logs and the component file.` **2. Multi-file architecture reasoning** When an issue spans multiple files and the AI loses track of the broader codebase context, send the workspace structure for expert analysis. - Sample prompt: `My authentication flow is broken across three files—authMiddleware.js, session.js, and login.tsx—and the AI can't connect the dots. Send a help request including these files and my workspace structure.` **3. Getting unblocked on architecture decisions** When you need guidance on a technical implementation strategy rather than just code, request expert advisory input. - Sample prompt: `I can't decide whether to use WebSockets or Server-Sent Events for my real-time notifications feature. Request help and include my current backend setup and dependencies so an expert can advise.` **4. Late-night launch blockers** When you're on a deadline and AI tools aren't cutting it, escalate immediately with full session context. - Sample prompt: `We're launching tomorrow and the payment integration keeps throwing a 400 error. Please request urgent help and include my conversation history, the Stripe integration files, and recent git diffs.` **5. Resolving issues after multiple failed AI attempts** When you've tried several AI-suggested fixes that didn't work, share those attempts so experts don't repeat them. - Sample prompt: `I've tried three different fixes for this CORS issue and none worked. Request help and include the solutions I already attempted along with their resulting errors so the expert has full context.` **6. Performance troubleshooting** When your app is slow and AI can't pinpoint the bottleneck, send performance metrics for human review. - Sample prompt: `My Node.js API endpoint is taking 8 seconds to respond and I can't figure out why. Request help and include CPU usage, memory metrics, and the relevant route handler file.` **7. Non-technical founder support** When you lack the technical depth to interpret AI output, hand off to an expert for a real conversation. - Sample prompt: `I'm not a developer and Cursor's suggestions are confusing me on this database migration. Request help from the Cleanup Crew team and include my project files and the migration script.` --- *Note: When triggering the `request_help` tool, avoid sending sensitive data such as API keys, credentials, or personal information in your context.*

The **ilert MCP server** provides a comprehensive interface for on-call management, incident response, and alert handling. ilert is an AI-first incident management platform used by companies like Lufthansa Systems, REWE Digital, and Bertelsmann to optimize incident management, improve reliability, and minimize downtime. This MCP server exposes tools across several key categories: **alert management** (listing, accepting, resolving, commenting, escalating, and rerouting alerts), **incident management** (creating, updating, and listing incidents), and **team coordination** (finding users, schedules, escalation policies, and services). It also supports invoking automated actions and workflows on alerts through webhook integrations. Together, these tools enable a full incident response lifecycle—from detecting and triaging alerts, coordinating responders, escalating to the right teams, to tracking and communicating major service disruptions through structured incidents. ## Use Cases ### 1. Reviewing and Managing Personal Alerts Combine `get-my-profile` and `list-alerts` (with the `assignedTo` parameter) to see alerts assigned to you and take action on them. - Sample prompt: `Show me all my open alerts that are currently pending or in progress` ### 2. Accepting and Resolving an Alert Use `show-alert-details`, `accept-alert`, `comment-alert`, and `resolve-alert` to work through an incident from start to finish. - Sample prompt: `Accept alert 4521, add a comment that I'm investigating the database connection issue, then resolve it once done` ### 3. Escalating an Alert to Another Level Use `show-alert-details` to see escalation levels, then `escalate-alert` when you can't handle the issue yourself. - Sample prompt: `Escalate alert 3390 to level 2 because I don't have access to the payment gateway systems` ### 4. Rerouting a Misassigned Alert Combine `find-escalation-policies` and `reroute-alert` to redirect an alert to the correct team. - Sample prompt: `Alert 2201 was sent to the wrong team — reroute it to the Network Operations escalation policy` ### 5. Adding Collaborators to an Incident Use `find-users` or `find-schedules` with `add-responder-to-alert` to bring in additional expertise. - Sample prompt: `Add responder Sarah Chen to alert 1180 so she can help troubleshoot the Kubernetes cluster` ### 6. Checking Who Is On Call Use `find-schedules` to see current and upcoming on-call rotations. - Sample prompt: `Who is currently on call for the backend team, and who is next in the rotation?` ### 7. Manually Creating an Alert Combine `find-alert-sources`, `find-escalation-policies`, and `create-alert` to raise an alert for an issue not caught by monitoring. - Sample prompt: `Create a high-priority alert titled "API latency spike in EU region" and route it through the SRE escalation policy` ### 8. Coordinating a Major Incident Use `find-services`, `create-incident`, and `update-incident` to track and communicate service disruptions. - Sample prompt: `Create a high-priority incident for a major outage on the Checkout service and set its status to INVESTIGATING` ### 9. Updating Incident Status Use `list-incidents` and `update-incident` to keep stakeholders informed as an incident evolves. - Sample prompt: `Find the open incident affecting the Payments service and update it to MONITORING with a note that a fix has been deployed` ### 10. Running Automated Remediation Actions Combine `list-alert-action` (or `list-alert-actions`) with `invoke-alert-action` to trigger automated workflows. - Sample prompt: `Show me the available actions for alert 5567 and run the "restart-service" webhook` ### 11. Finding Team Members by Role Use `find-users` filtered by role to locate the right people for a task. - Sample prompt: `Find all users with the RESPONDER role who have "database" in their name or email` ### 12. Auditing Recent Alerts by Date Range Use `list-alerts` with `from` and `until` parameters for reporting and review. - Sample prompt: `List all resolved alerts from January 1st to January 31st, 2025 for our post-incident review`

The Context7 MCP server, developed by Upstash, provides AI agents with access to up-to-date documentation and code examples for programming libraries and frameworks. Trusted by teams at companies like OpenAI, Netflix, Vercel, and Google, Context7 maintains a massive index of over 118,000 libraries to ensure AI coding assistants always work with current, accurate documentation. The server exposes two complementary tools that work together in a streamlined workflow. The first tool, `resolve-library-id`, translates a plain-language library or package name into a Context7-compatible identifier, ranking matches by name similarity, source reputation, documentation coverage, and benchmark score. The second tool, `query-docs`, retrieves targeted documentation and code snippets for a specific library concept using that identifier. Together, they eliminate outdated or hallucinated code by grounding AI responses in the latest official documentation, making it ideal for use in agents like Claude, Cursor, and Codex. ## Use Cases **1. Resolving the correct library reference** When you're unsure of the exact package name or need to find the authoritative source for a library, the resolve tool identifies the best-matching Context7 ID. - Sample prompt: `Find the Context7 library ID for the Next.js framework by Vercel so I can pull its documentation.` **2. Retrieving up-to-date framework documentation** Fetch current documentation for a specific feature to avoid relying on the model's outdated training data. - Sample prompt: `Get the latest Next.js App Router documentation and show me how to set up dynamic route segments with the /vercel/next.js library.` **3. Getting accurate code examples for implementation** Pull real, working code snippets scoped to a single concept to accelerate development. - Sample prompt: `Show me current code examples for setting up authentication with JWT in Express.js using the official docs.` **4. Version-specific documentation lookup** Query documentation for a particular version of a library to match your project's dependencies. - Sample prompt: `Retrieve the useEffect cleanup function documentation from React version 18 specifically.` **5. Comparing libraries before adoption** Use the resolve tool to compare multiple matching libraries by reputation, snippet coverage, and benchmark score before committing. - Sample prompt: `Search for available ORM libraries for TypeScript and tell me which one has the best documentation coverage and source reputation.` **6. Debugging with authoritative guidance** Combine both tools to diagnose issues by consulting the official documentation for a specific behavior. - Sample prompt: `I'm getting a hydration error in Next.js. Resolve the library and find the official docs on server-side rendering and hydration mismatches.` **7. Learning a new library from scratch** Fetch structured documentation to onboard quickly onto an unfamiliar technology. - Sample prompt: `I'm new to Supabase. Find the library and get me the documentation on setting up row-level security policies.` **8. Exploring integration between two concepts** Query how two features of a library interact within a single, focused request. - Sample prompt: `Using the Prisma docs, explain how migrations and the schema definition work together in a PostgreSQL setup.`