Turn any documentation site into an MCP server
Wellknown found it in public sources; nobody has proven control of it yet. Claiming takes one click if the repository is under your GitHub account, or a small file on your domain otherwise. Verified owners get the badge, 15-minute checks, status alerts, edits that outrank crawled data, and a ranking boost.
Agents can do it too: POST https://wellknown.network/api/v1/claims with {"agent":"mcp-docs-contributors-mcp-docs","method":"well_known_file"} — machine-readable steps at claim.json, guide at /docs/claim.
Everything here was measured by our prober or read from a registry. Nothing is self-reported.
Attributed to the source that supplied each field. Treated as claims, not facts.
# MCP-Docs — Turn any docs site into an MCP server A Python package that scrapes documentation sites, indexes them with embeddings, and exposes them as MCP (Model Context Protocol) servers for LLM integration. ## Features - **Web Scraping**: Uses Playwright to scrape JavaScript-rendered documentation sites - **Semantic Search**: Indexes documentation with embeddings for semantic search - **Multiple Embedding Providers**: Supports OpenAI and Azure OpenAI embeddings - **Vector Storage**: Uses ChromaDB for persistent vector storage - **MCP Server**: Automatically generates and runs MCP servers for each documentation project - **Project-Based**: Organizes documentation sites as separate projects - **CLI Tool**: Simple command-line interface for managing projects ## Installation ### From Source ```bash # Clone the repository git clone https://github.com/yourorg/mcp-docs.git cd mcp-docs # Create virtual environment python -m venv .venv # Activate virtual environment # On Windows (PowerShell): .\.venv\Scripts\Activate.ps1 # On macOS/Linux: source .venv/bin/activate # Install in editable mode pip install -e ".[dev]" # Install Playwright browsers playwright install chromium ``` ### Dependencies The project requires: - Python 3.8+ - Embedding provider credentials: - OpenAI API key, OR - Azure OpenAI (API key, endpoint, and deployment ID) - Playwright (for web scraping) - ChromaDB (for vector storage) ## Quick Start ### 1. Configure Embedding Provider Set up your embedding provider (OpenAI or Azure OpenAI): ```bash # Interactive configuration (will prompt for provider selection) mcp-docs configure ``` Or configure via command line: ```bash # Configure OpenAI mcp-docs configure --provider openai --api-key sk-your-key-here # Configure Azure OpenAI mcp-docs configure --provider azure-openai --api-key your-key --endpoint https://your-resource.openai.azure.com --deployment-id text-embedding-a…
Mapped onto the structured taxonomy from declared text and observed tool names. Confidence shown for derived entries.
Every source is kept verbatim. Field changes are logged as events.