What are the runtime dependencies?
Ragdocs requires Node.js, a Qdrant vector database (local or cloud), and an embeddings provider – either a local Ollama instance or an OpenAI API key.
M
MCP Server Ragdocs
@sanderkooger
Basic information
About MCP Server Ragdocs
An MCP server that provides tools for retrieving and processing documentation through vector search, both locally or hosted. Enabling AI assistants to augment their responses with relevant documentation context.
MCP Server Ragdocs config
No standard config provided
This server doesn't expose a parseable MCP config block in its README. See the repository for install instructions.
RepositoryMCP Server Ragdocs tools
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Overview
What is Ragdocs?
Ragdocs is an MCP server that provides tools for retrieving and processing documentation through vector search, enabling AI assistants to augment their responses with relevant documentation context.
How to use Ragdocs?
Install and run it via npx -y @sanderkooger/mcp-server-ragdocs. Configure the server with environment variables for your embeddings provider (Ollama or OpenAI) and Qdrant vector database. Add the configuration to your MCP host (e.g., Claude Desktop) using the provided JSON snippets.
Key features of Ragdocs
- Vector-based documentation search and retrieval
- Support for multiple documentation sources
- Local (Ollama) or OpenAI embedding generation
- Automated documentation processing and indexing
- Real-time context augmentation for LLMs
Use cases of Ragdocs
- Enhancing AI responses with relevant documentation
- Building documentation-aware AI assistants
- Creating context-aware tooling for developers
- Implementing semantic documentation search
- Augmenting existing knowledge bases
Frequently asked questions
How can I deploy Ragdocs locally or in the cloud?
For local development, use the provided Docker Compose file to start Qdrant and Ollama. For production, use a hosted Qdrant Cloud service and set `QDRANT_URL` and `QDRANT_API_KEY`.
Which embeddings providers are supported?
Ragdocs supports Ollama (using the `nomic-embed-text` model) and OpenAI as embeddings providers, configured via the `EMBEDDINGS_PROVIDER` environment variable.
How do I use Ragdocs with Claude Desktop?
Add a JSON entry under `mcpServers` in your `claude_desktop_config.json`, specifying the command, arguments, and environment variables for your chosen provider and Qdrant instance.
What tools does Ragdocs expose?
It provides `search_documentation`, `list_sources`, `extract_urls`, `remove_documentation`, `list_queue`, `run_queue`, and `clear_queue` for managing and querying documentation.
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