arabold/docs-mcp-server

Grounded Docs gives your AI coding assistant a documentation index that matches the library versions you use. It fetches documentation from websites, GitHub repositories, npm, PyPI and local folders, and serves search over the Model Context Protocol (MCP), a command line and a web UI. Your assistant answers from that documentation instead of from what it remembers.
It is an open-source alternative to Context7, Nia and Ref.Tools.
## Features
Indexes websites, GitHub repositories, npm and PyPI packages, local folders and archives (ZIP, TAR).
Reads HTML, Markdown, PDF, Office and OpenDocument files, EPUB, Jupyter notebooks, source code and more. See Supported Formats.
Stores each library version separately, so a search targets the version your project uses.
Combines full-text search with optional vector search through OpenAI, Ollama, Gemini, Vertex AI, AWS Bedrock, Azure OpenAI or any OpenAI-compatible server.
Runs on your machine and keeps its index in a local SQLite database. Document text goes only to the embedding provider you configure; with a local one, such as Ollama, it stays on your machine. Usage telemetry is on by default; Telemetry lists what it sends and how to turn it off.
Works with any MCP client, over stdio or Streamable HTTP.
## Quick Start
You need Node.js 22 or later, or Docker. Installation covers both in detail, along with Docker Compose and running the server inside your AI client.
### 1. Start the server
bash
npx @arabold/docs-mcp-server@latest
Or with Docker:
bash
docker run --rm \
-v docs-mcp-data:/data \
-v docs-mcp-config:/config \
-p 6280:6280 \
ghcr.io/arabold/docs-mcp-server:latest
### 2. Turn on vector search (optional)
Without an embedding model, search uses full-text matching. Vector search also finds passages by meaning. To use OpenAI, start the server with OPENAIAPIKEY set:
bash
OPENAIAPIKEY="sk-proj-..." npx @arabold/docs-mcp-server@latest
With Docker, add -e OPENAIAPIKEY="sk-proj-...". Embedding Models covers Ollama, Gemini, Azure OpenAI and other providers.
Do this before you add documentation. Embeddings are computed when a page is indexed, so documentation indexed without a model gets no vectors until you scrape it again with --replace.
### 3. Add documentation
Open the web UI at http://localhost:6280 and choose Add library.
### 4. Connect your AI client
Add the server's MCP endpoint, http://localhost:6280/mcp, to your client. Most clients accept an entry like this:
json
"mcpServers": {
"docs-mcp-server": {
"url": "http://localhost:6280/mcp"
Connecting MCP Clients has the exact settings for Claude Code, Claude Desktop, Zed and other clients, how a client can start the server itself over stdio, and the list of MCP tools.
## Command Line
The CLI indexes and searches without a running server, which suits scripts and coding agents. It shares its database with a server on the same machine.
bash
# Index documentation
npx @arabold/docs-mcp-server@latest scrape react https://react.dev/reference/react
# Search it
npx @arabold/docs-mcp-server@latest search react "useEffect cleanup" --output yaml