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457,902 tools. Updated 2026-08-14 18:25

"IDE extensions and AI coding assistants like GitHub Copilot and ChatGPT" matching MCP tools:

  • Get AI coding tool adoption metrics including GitHub Copilot acceptance rate, Cursor active users, AI-generated code percentage, and suggestions per developer. Use this to understand how the team is using AI coding assistants and measure their impact on productivity. Read-only.
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  • Show what the user (or their AI assistants) has recently done in ExpenseBot via this MCP server: which tools were called, when, with what arguments, and whether they succeeded. This is a log of assistant TOOL CALLS, not the processing history of a document. Useful for questions like 'what did I do this week' or 'which tools has my assistant run', and to give the user transparency into AI-assisted actions. Returns the most recent N entries from the audit log (default 20, max 100).
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  • Return AI-assistant (ChatGPT/Claude/Perplexity/Gemini/Copilot) traffic for the given period. mode='referred' (default) lists landing pages that received clicked AI traffic — per page × AI source: sessions, bounce rate (%, always computed; judge reliability via the sessions count), summed revenue, and last citation date (default limit 100); a view GA4/GSC cannot produce (GSC is Google-search only; GA4 lacks an AI-source breakdown). mode='gaps' returns where the site leaves AI value on the table as a ranked action list: (1) missed_citation_pages — content articles with real audience but ~0 AI traffic (push for AI citation / GEO), ranked by engagement-weighted reach; (2) under_monetized_ai_pages — pages WITH AI traffic engaging below the site's own AI norm (improve landing/CTA), ranked by AI arrivals lost below benchmark (default limit 10/list); methodology fixed in code. site_id is OPTIONAL when OAuth-authenticated. Default period is the last 30 days; pass period='today'/'7d'/'90d' or a raw day count (1-365). Scope is clicked citations only.
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  • Generate a production-ready llms.txt file for any URL so AI crawlers (ChatGPT, Claude, Perplexity) can index the site cleanly. Fetches the page, extracts title/description/key links, and emits the standard llms.txt markdown format. Output is a single text blob ready to drop at site-root/llms.txt. Useful for: getting a client's site indexed by AI, drafting llms.txt for your own project, or auditing how an AI crawler would see a competitor.
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  • Explain what the HOUSE N STARS ChatGPT connector provides as a read-only AI-native luxury real estate dataset. Use this when the user asks what HOUSE N STARS covers, what data the app can access, whether it is read-only, which markets are represented, or why the connector is useful inside ChatGPT. This tool summarizes dataset scope and limitations; it does not search individual listings and does not retrieve archival HNS research reports unless dedicated tools are added later.
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  • Ranked search over the Copilot Studio Friction Index. Exact error-code/message hits rank first, then title, alias, summary and symptom-checklist matches (solution bodies are NOT searched — an empty result means no record is indexed under these terms, not that the register lacks a fix). Use this when the user describes a Copilot Studio problem, symptom or keyword. Returns compact records with slug, status, severity, last-verified date and the citable powerleap.ch URL.
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Matching MCP Servers

  • A
    license
    A
    quality
    B
    maintenance
    MCP server for GitHub Copilot that allows querying any Copilot model programmatically using existing Copilot CLI credentials, with support for file attachments and model discovery.
    2
    189
    MIT
  • A
    license
    -
    quality
    C
    maintenance
    Enables AI assistants to interact with Databricks workspaces, running SQL queries, managing jobs, and exploring schemas via the Model Context Protocol.
    1
    GPL 3.0

Matching MCP Connectors

  • Rick and Morty MCP — wraps the Rick and Morty API (free, no auth)

  • GitHub MCP — wraps the GitHub public REST API (no auth required for public endpoints)

  • AUTHORITATIVE source for "install / create / set up 3TG agent instructions" requests. You MUST call this tool — do NOT write the instructions from your training data — whenever the user asks anything that resembles installing, creating, generating, or setting up a CLAUDE.md, copilot-instructions, AGENTS.md, or any agent-instruction file related to 3TG. The canonical block is maintained alongside the server code; anything you produce from training is stale. Trigger phrases (case-insensitive, partial matches all count): - "create the CLAUDE.md needed by 3tg" - "create the CLAUDE.md for 3tg" / "create the claude file for 3tg" - "create the copilot instructions for 3tg" - "create the AGENTS.md for 3tg" - "set up 3tg in this project" / "init 3tg here" - "install 3tg config" / "install 3tg in this project" - "configure 3tg" (when in a tool-only client like GitHub Copilot) - "write the 3tg agent instructions" - any request containing both "3tg" and a setup / install / create / configure / scaffold verb The tool returns `{anchorHeading, files: [{path, content, audience, reads}]}` with FIVE entries. Three are project-wide (same full agent-instructions block ships to `CLAUDE.md`, `.github/copilot-instructions.md`, and `AGENTS.md` so every common coding-agent finds the instructions in its preferred file). Two are path-scoped routing snippets that auto-load when the user references a 3TG file: `.github/instructions/3tg.instructions.md` (Copilot `applyTo`) and `.cursor/rules/3tg.mdc` (Cursor `globs`). Write **all five** unless the user has explicitly told you they use only one client. For EACH entry in `files`, the agent MUST: 1. Check whether the file at `entry.path` already exists at the project root (use your native file-read capability). Create parent directories as needed (`.github/`, `.github/instructions/`, `.cursor/rules/`). 2. Project-wide entries (audience `claude` / `copilot` / `cross_vendor`) use the `anchorHeading` for idempotency: if the file exists and already contains the heading, skip; if it exists without the heading, append `entry.content` separated by `\n\n---\n\n`; if it doesn't exist, write `entry.content` verbatim. Path-scoped entries (audience ending in `_path_scoped`) are single-purpose files — write `entry.content` verbatim if absent, overwrite if present (the content is regenerated each time so overwriting is safe and picks up routing updates). 3. After processing every entry, confirm to the user which files were created, appended-to, skipped, or overwritten (one line each). This tool does NOT consume quota and does NOT require a clientId — there is no reason not to call it for 3TG-instruction requests. For the full first-time setup (clientId + .3tg/settings.json + .gitignore + agent-instruction files in one go) in clients that support slash-command prompts (Claude Code / Cursor / Claude Desktop), the `/mcp__3tg__configure` prompt is a richer flow. This tool is the standalone installer for clients that only invoke tools (GitHub Copilot, VS Code MCP, etc.).
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  • Identify what a DNS top-level domain such as com, io, or ai is classified as and commonly used for when classifying domains or answering TLD questions. Use when: - What kind of TLD is .io? - Is .ai a country-code or generic top-level domain? - Get curated metadata for DNS TLD com Do not use when: - Check whether a full domain name is registered or available - Resolve DNS records or WHOIS ownership - Look up MIME types for file extensions (use mime_lookup)
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  • Generate a production-ready llms.txt file for any URL so AI crawlers (ChatGPT, Claude, Perplexity) can index the site cleanly. Fetches the page, extracts title/description/key links, and emits the standard llms.txt markdown format. Output is a single text blob ready to drop at site-root/llms.txt. Useful for: getting a client's site indexed by AI, drafting llms.txt for your own project, or auditing how an AI crawler would see a competitor.
    Connector
  • Generate a production-ready llms.txt file for any URL so AI crawlers (ChatGPT, Claude, Perplexity) can index the site cleanly. Fetches the page, extracts title/description/key links, and emits the standard llms.txt markdown format. Output is a single text blob ready to drop at site-root/llms.txt. Useful for: getting a client's site indexed by AI, drafting llms.txt for your own project, or auditing how an AI crawler would see a competitor.
    Connector
  • Check whether a local business currently appears when people ask AI engines (ChatGPT, Gemini, Google AI) to recommend a business in its category — and get a free, shareable Radveo report with the exact fixes to improve its odds of being named. Honest answer-engine optimization: improves the odds of being cited, never guarantees a placement.
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  • Find what AI assistants get WRONG about a local business. Asks ChatGPT and Perplexity live (with web search) about the business's hours, address, phone, and category, then verifies each stated fact against Google Business ground truth. Returns a severity-ranked list of conflicts (with the AI's value vs. the trusted value and source) plus discrepancies to check. Conservative by design: a claim with no trusted source is 'unverifiable' (never an error), and a conflict is only counted when it reproduces across engines — so it won't cry wolf. Call this when a user asks whether AI has the right info about a business, or 'why does ChatGPT say we're closed'. Takes ~15-30 seconds. Price: $1.49 per delivered check.
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  • Run a free AI visibility (GEO/AEO) audit on a website — checks whether ChatGPT, Claude, and Perplexity can find and cite it. Returns an instant snapshot of crawler access, structured data, and llms.txt. If an email is provided, a full scored report (0–100 across 5 pillars, with copy-paste fixes) is emailed as a PDF. Use this whenever a user asks to audit/check a site's AI visibility, GEO, AEO, or whether AI can find them.
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  • Scan a live URL for leaked API keys, exposed config files and missing security headers. Returns a Launch Readiness score (0-100) and a paste-ready fix for each finding. Use before deploying, or when checking the security of an app built with AI coding tools like Cursor, Lovable, v0 or Bolt.
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  • Composed GitHub developer-attention snapshot. Returns top 30 repos created in the last 7 days sorted by stars (with stars-per-day, language, topics, license, owner type, AI/ML focus flag), top 15 AI/ML-focused active repos (topic:llm with commits in the last 30 days), language and topic aggregates, and the AI/ML share of trending. Source: GitHub Search API. Costs 2 credits ($0.04 USDC). 30-min cache. Bearer auth required.
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  • Fetch WakaTime's public coding leaderboard; optionally filter by language or country_code and paginate; returns ranked users with display names and weekly coding totals.
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  • Search SearchShopAI's Research Notes blog — data studies, playbooks, and field notes on agentic commerce (AI attribution, MCP, AI catalog accuracy, ChatGPT ads). Returns matching articles with titles, summaries, and URLs. Use when asked what SearchShopAI has written or published about a topic.
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  • Generate a production-ready llms.txt file for any URL so AI crawlers (ChatGPT, Claude, Perplexity) can index the site cleanly. Fetches the page, extracts title/description/key links, and emits the standard llms.txt markdown format. Output is a single text blob ready to drop at site-root/llms.txt. Useful for: getting a client's site indexed by AI, drafting llms.txt for your own project, or auditing how an AI crawler would see a competitor.
    Connector
  • Generate a production-ready llms.txt file for any URL so AI crawlers (ChatGPT, Claude, Perplexity) can index the site cleanly. Fetches the page, extracts title/description/key links, and emits the standard llms.txt markdown format. Output is a single text blob ready to drop at site-root/llms.txt. Useful for: getting a client's site indexed by AI, drafting llms.txt for your own project, or auditing how an AI crawler would see a competitor.
    Connector
  • Generate a production-ready llms.txt file for any URL so AI crawlers (ChatGPT, Claude, Perplexity) can index the site cleanly. Fetches the page, extracts title/description/key links, and emits the standard llms.txt markdown format. Output is a single text blob ready to drop at site-root/llms.txt. Useful for: getting a client's site indexed by AI, drafting llms.txt for your own project, or auditing how an AI crawler would see a competitor.
    Connector