Answer questions about documentation using AI. Narrow context by providing specific document paths and control context length.
MIT
305,560 tools. Last updated 2026-07-23 07:10
"An MCP for extracting and analyzing documentation using AI" matching MCP tools:
- Add LPM packages to your project by extracting source files for customization. Use for UI components, blocks, templates, and MCP servers.ISC
- Search MCP Protocol and FastMCP documentation to find relevant guides for building MCP servers, with ranked results and optional source filtering.MIT
- Generate a standards-compliant SBOM for your AI agent infrastructure by discovering agents and MCP servers and extracting all package dependencies.Apache 2.0
- Retrieve documentation for n8n MCP tools. Use without parameters for a quick start guide, or specify a topic and depth for detailed documentation.MIT
- Extract web content and convert it to clean Markdown for reading documentation, analyzing content, and gathering information from websites while preserving links and structure.MIT
Matching MCP Servers
- Alicense-qualityDmaintenanceProvides AI assistants with a standardized interface to interact with the Todo for AI task management system. It enables users to retrieve project tasks, create new entries, and submit completion feedback through natural language.Last updated9Apache 2.0
- AlicenseAqualityAmaintenance50 tools and 400 functions for working with Excel/.xlsx spreadsheets — read/write, recalculate formulas, diff, repair broken references, and audit. Built for AI agents.Last updated508064MIT
Matching MCP Connectors
MCP server for Vonage API documentation, code snippets, tutorials, and troubleshooting.
Honeydew AI Documentation MCP — semantic search and ripgrep-grade filesystem queries over Honeydew AI docs and OpenAPI specs, for AI coding agents.
- Provides best-practice guidance for users and AI agents using this MCP server, focusing on safe codebase access, file discovery, and memory.MIT
- Refresh Slack session tokens for AI agents by extracting them from an open Chrome tab.MIT
- Extract web content and convert it to clean Markdown format for reading documentation, analyzing information, and gathering data from websites while preserving links and structure.Apache 2.0
- Authenticate with existing AI-Archive credentials to generate an API key for MCP client access.MIT
- Validates an OpenAPI specification for AI agent compatibility, assessing documentation quality and providing a score with recommendations for improvement.MIT
- Initializes Next.js DevTools MCP context, resets AI knowledge base, and establishes mandatory documentation-first approach for Next.js development.MIT
- Analyze project structure and documentation to provide essential context for AI assistants, enabling efficient workflow integration. Accepts project path or uses current directory.
- Search MCP documentation by keywords or phrases to find relevant sections and context for development workflows, server building, and client implementation.MIT
- Search OSCAL documentation for answers to questions that cannot be resolved by analyzing model schemas alone. Get detailed information on specific OSCAL properties and usage.Apache 2.0
- Generate and save detailed explanations for software-related queries using official documentation, powered by Vertex AI Gemini models. Input topic, query, and output path for results.MIT
- Store MCP documentation securely by saving text with a unique key for easy retrieval and future reference, enhancing project organization on the MCP Maker server.Apache 2.0
- Extract documentation components, APIs, and usage examples from website links using configurable crawling depth and content selectors.MIT
- Analyze documentation quality by checking for duplicates, relevance, and completeness using AI-powered insights to improve content accuracy.MIT
- Answer questions about documentation using AI with context from documents. Get AI-generated answers with sources for technical queries.MIT