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MCP Server Ragdocs

@sanderkooger

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基本情報

カテゴリ

メモリとナレッジ

トランスポート

stdio

作者

sanderkooger

投稿者

Sander Kooger

MCP Server Ragdocs について

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 の設定

標準の設定はありません

このサーバーの README には解析可能な MCP 設定ブロックが含まれていません。インストール手順はリポジトリをご確認ください。

リポジトリ

MCP Server Ragdocs のツール

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この掲載には表示できるツール一覧がまだありません。サーバーにツールがないという意味ではありません。対応機能はプロジェクトのドキュメントをご確認ください。

ツールは README から自動的に抽出されます。メンテナーは ## Tools という見出しの下に記載することで、このタブに反映できます。

概要

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

よくある質問

1

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.

2

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`.

3

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.

4

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.

5

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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