Best for
- Developers who want private, self-hosted, version-accurate documentation grounding for AI coding assistants
- Teams wanting to index internal or third-party docs without sending code to external hosted services
D
Docs MCP Server (Grounded Docs)
Community
Self-hosted, version-accurate documentation grounding for AI coding assistants.
GitHub source repository ↗
65FMRS · C
A well-documented, MIT-licensed open-source MCP server with broad file-format support and CLI, Web UI, and MCP interfaces; not officially maintained by a protocol or product owner (community project by arabold, positioned as an alternative to Context7/Nia/Ref.Tools), with the README's explicit client config example covering only the SSE transport.
Docs MCP Server (Grounded Docs) is a self-hosted MCP server that fetches and indexes up-to-date, version-specific documentation from websites, GitHub, npm, PyPI, and local files so AI coding assistants can query real documentation instead of relying on stale training data. It provides a Web UI, a CLI, and an MCP endpoint (including a scrape_docs tool) for indexing and searching documentation, with optional embedding-model support for semantic search.
Tools
scrape_docs
Scrapes a given URL and indexes its documentation content into the server's search index; supports a preserveHashes option for documentation sites that use hash-based client-side routing (SPAs).
Setup
Run npx @arabold/docs-mcp-server@latest (requires Node.js 22+) to start the server, then open http://localhost:6280 to add documentation via the Web UI. Alternatively, run the Docker image ghcr.io/arabold/docs-mcp-server:latest with --protocol http --host 0.0.0.0 --port 6280. Add the server to your MCP client config (e.g. claude_desktop_config.json) pointing at the SSE endpoint http://localhost:6280/sse. Optionally set an embedding-provider API key (e.g. OPENAI_API_KEY) before starting to enable semantic search.
claude_desktop_config.json
{
"mcpServers": {
"docs-mcp-server": {
"type": "sse",
"url": "http://localhost:6280/sse"
}
}
}
Fit and risk
Not for
- Users wanting a zero-setup hosted service without running their own server
- Users needing immediate answers for documentation that hasn't been scraped/indexed yet
- Use cases requiring guaranteed 100% accurate retrieval with no keyword/semantic search misses
Required permissions
- Outbound network access to fetch documentation from websites, GitHub, npm, and PyPI
- Read access to local files, folders, and archives when indexing local sources
- Local disk/storage for the documentation index (Docker deployments use /data and /config volumes)
- Network access and an API key to a third-party embedding provider (e.g. OpenAI) if semantic search is enabled
- Binding a local network port (default 6280) to serve the Web UI and MCP endpoint
Risks and side effects
- Fetching and indexing arbitrary external URLs can pull in untrusted or malicious content
- Binding the server to 0.0.0.0 without configuring authentication (OAuth2/OIDC) can expose the Web UI, MCP endpoint, and indexed documentation to anyone on the network
- Processing untrusted archives, Office documents, and PDFs carries the general risk of file-parsing bugs
- Embedding-provider API keys (e.g. OPENAI_API_KEY) are supplied via environment variables and sent to the third-party provider
Troubleshooting
- Verify Node.js 22+ is installed for the npx CLI
- If the Web UI doesn't load, confirm port 6280 is free and reachable
- Use --verbose for debug logs, or --quiet to suppress non-error diagnostics in non-interactive runs
- For hash-routed SPA documentation sites, enable --preserve-hashes (CLI) or the preserveHashes option (MCP/Web UI)
- If semantic search quality is poor, confirm an embedding-provider API key (e.g. OPENAI_API_KEY) is set
- If indexed data doesn't persist across restarts, check Docker volume mounts (-v docs-mcp-data, -v docs-mcp-config)
- If exposing the server beyond localhost, review the security and authentication documentation to configure access control
Use cases
Build an up-to-date documentation index for libraries/frameworks to reduce LLM hallucinations
Let an AI coding assistant query docs for the exact library version used in a project
Index local files, zip/tar archives, PDFs, and Office documents to build a private documentation search index
Query indexed documentation from MCP clients such as Claude Desktop or VS Code (Cline/Roo)
Supported clients
Claude DesktopFull support
ClinePartial support
VS CodePartial support
GitHub CopilotPartial support
Gemini CLIPartial support