"Documentation for Libraries" matching MCP servers:

An MCP server that retrieves and cleans official documentation for popular AI/Python libraries via web search and LLM processing, exposing a get_docs tool.

An Operit-compatible adapter of the Exa MCP server, enabling web search, code search, and company research capabilities in AI assistants. It fixes MCP handshake compatibility issues, allowing tools like web_search_exa and web_fetch_exa to load and run reliably.

TypeScript implementation of an MCP server that provides tools for web crawling, content extraction, and browser automation, enabling AI systems to access and process web content through 15 specialized tools.

Enables intelligent navigation and extraction of documentation from websites, allowing Amazon Q to automatically discover relevant pages, extract clean content, and retrieve code examples from web documentation.

Enables AI assistants to generate alt text, manage image libraries, and audit web pages for accessibility using the AltText.ai API.

Enables AI agents and compatible MCP clients to fetch, search, and download web content—including .onion services—anonymously through the Tor network, using a fresh circuit per call and retrying when exit nodes are blocked.

Enables hybrid web search and intelligent content extraction, combining semantic search with documentation-optimized reading that strips noise and returns clean, token-efficient context for AI agents.

An MCP server that lets AI coding assistants list, search, and read the public Quasar Store documentation in real time.

Enables AI clients to search live documentation for popular developer libraries and return clean, source-cited summaries via a RAG pipeline.

Scrapes, indexes, and serves Salesforce Architect documentation locally, enabling fast offline RAG-powered search and retrieval for AI coding assistants.

An MCP server that lets AI assistants like Claude read LinkedIn data through your own logged-in browser session. Access profiles and companies, search for jobs, or get job details.

A fault-tolerant, stealth-enabled Model Context Protocol (MCP) server for web searching and content fetching. Built for AI Agents (Cursor, Claude Code, OpenCode), it uses a stealth browser engine to fetch pages, dynamically handles SPAs/React, and converts bloat into token-optimized Markdown.

This server crawls documentation sites and provides search and page-retrieval tools through MCP, enabling users to query indexed documentation content.

An MCP server that lets AI assistants like Claude read LinkedIn data through your own logged-in browser session. Access profiles and companies, search for jobs, or get job details.

MCP server that allows AI agents to fetch and process llms.txt documentation from various sources. Fetch documentation from any HTTPS URL and automatically convert HTML content to readable markdown.

Enables MCP-aware agents to fetch documentation pages, strip navigation noise, convert HTML to clean markdown, and emit or return timestamped .md files ready for LLM contexts.

Enables LLMs to scrape, index, and search modern and legacy Salesforce documentation by piercing complex Shadow DOM and iframe structures. It supports hierarchical spidering of entire guides and local RAG capabilities using a SQLite database for offline querying.

MCP server that provides access to Livewire Flux components and layouts documentation, enabling AI assistants to fetch and search through documentation on demand.

Scrapes documentation sites, chunks and embeds content locally, then retrieves relevant passages for queries. Enables agents to ask questions grounded in documentation.

Scrapes, stores, and searches documentation locally, enabling AI assistants to access and query documentation via MCP.