MCP Server Ragdocs

@sanderkooger

代码仓库

基本信息

分类

记忆与知识

传输方式

stdio

作者

sanderkooger

提交者

Sander Kooger

关于 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 #documentation #vector-search

MCP Server Ragdocs 配置

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代码仓库

MCP Server Ragdocs 工具

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概览

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

常见问题

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.

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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