"Documentation" matching MCP servers:

Enables AI agents to search and query documentation from git repositories using hybrid search and structured metadata queries.

Exposes an internal engineering knowledge base to AI assistants, allowing users to search and retrieve standards, runbooks, and architecture decisions. It supports RAG-enhanced search, document scraping, and specialized prompts for incident investigation and code reviews.

Local-first document management and semantic search for AI coding agents, with a built-in web dashboard and REST API.

Enables semantic search over local Markdown documentation using hybrid retrieval combining embeddings, keyword search, and graph traversal with automatic file watching and zero-configuration setup.

Enables AI agents to manage, upload, chunk, and semantically search local documents through MCP tools and a REST API, with a built-in web dashboard and optional Gemini-powered AI search.

Enables semantic search and retrieval of MCP (Model Context Protocol) documentation using Redis-backed embeddings, allowing users to query and access documentation content through natural language.

Provides tools for retrieving and processing documentation through vector search, enabling AI assistants to augment their responses with relevant documentation context.

An MCP server that serves documentation and enables AI-powered search, Q\&A, and document analysis for developer tools and guides.

Enables retrieval and cleaning of official documentation content for popular AI/Python libraries (uv, langchain, openai, llama-index) through web scraping and LLM-powered content extraction. Uses Serper API for search and Groq API to clean HTML into readable text with source attribution.

Enables AI-powered querying and serving of markdown documentation with search, Q\&A capabilities, and document analysis. Built for the YC Agents Hackathon with OpenAI integration and rate limiting protection.

Transforms any documentation website (GitBook, Vercel docs, Next.js sites, etc.) into an AI-accessible knowledge base with intelligent caching and automatic domain detection.

Provides instant access to Exa's neural search API documentation, code examples, and integration guides through natural language queries, enabling faster development of search, news monitoring, and RAG applications.

Provides semantic search capabilities over PyTorch documentation, enabling users to find relevant documentation, APIs, code examples, and error messages through Claude Code integration.

Enables AI assistants to search and explore Databricks AWS documentation through semantic search and 10 MCP tools.

Enables AI assistants to enhance their responses with relevant documentation through a semantic vector search, offering tools for managing and processing documentation efficiently.

Enables serving and querying documentation with AI-powered capabilities including markdown file serving, keyword search, and intelligent Q\&A using OpenAI integration.

Enables serving and querying documentation with AI capabilities, allowing users to search, ask questions, and get AI-powered answers from their documentation files. Built for seamless deployment on the Dedalus platform with OpenAI integration for enhanced document analysis.

Enables semantic search over Koog documentation using vector-based RAG, allowing users to ask questions and retrieve relevant sections with context.

Enables querying the SMRT framework documentation by automatically routing questions to relevant package experts and synthesizing responses from their CLAUDE.md files using AI-powered retrieval-augmented generation.

Enables searching and fetching LlamaIndex documentation for integration with VS Code Copilot and other MCP clients.