What environment variables are required?
You need `OPENAI_API_KEY` for embeddings generation, `QDRANT_URL` for your Qdrant vector database instance, and `QDRANT_API_KEY` for authenticating with Qdrant.
RAG Documentation MCP Server
@hannesrudolph
基本情報
RAG Documentation MCP Server について
An MCP server implementation that provides tools for retrieving and processing documentation through vector search, enabling AI assistants to augment their responses with relevant documentation context.
RAG Documentation MCP Server の設定
以下の設定を使って、このサーバーを MCP 対応クライアントに追加してください。
{
"mcpServers": {
"rag-docs": {
"command": "npx",
"args": [
"-y",
"@hannesrudolph/mcp-ragdocs"
],
"env": {
"OPENAI_API_KEY": "",
"QDRANT_URL": "",
"QDRANT_API_KEY": ""
}
}
}
}RAG Documentation MCP Server のツール
ツール一覧はまだありません
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。
上のボタンで一時的なサンドボックスでこのサーバーを実行し、ツール一覧を取得できます。
概要
What is RAG Documentation MCP Server?
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. It supports multiple documentation sources and uses semantic search to find relevant excerpts.
How to use RAG Documentation MCP Server?
Install the server via npx and add it to your claude_desktop_config.json with the required environment variables (OPENAI_API_KEY, QDRANT_URL, QDRANT_API_KEY). Once configured, use the provided tools such as search_documentation to query indexed documentation or extract_urls to add new sources.
Key features of RAG Documentation MCP Server
- Vector-based documentation search and retrieval
- Support for multiple documentation sources
- Semantic search capabilities
- Automated documentation processing
- Real-time context augmentation for LLMs
Use cases of RAG Documentation MCP Server
- 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
How do I search documentation?
Use the `search_documentation` tool with a natural language query and an optional `limit` (1–20, default 5) to receive ranked excerpts with context.
How can I add new documentation sources?
Use the `extract_urls` tool on a public webpage to find hyperlinks, optionally adding them to the processing queue. Then run the `run_queue` tool to index them.
How do I remove documentation?
Use the `remove_documentation` tool with an array of exact URLs. Removal is permanent and affects future search results.
Can I monitor the processing queue?
Yes, use `list_queue` to see pending URLs and `clear_queue` to remove all pending items immediately.
「メモリとナレッジ」の他のコンテンツ
agentleFS
agentleFSOne permissioned folder your team and your AI agents both log into.
server-noonien - shared memory MCP
sequicoShared, serverless, conflict-free memory for AI agents: a multi-machine drop-in for the MCP memory knowledge graph.
Takibi Base
Deian IsacAsk your Takibi Base collections anything; get answers with cited passages and a support score.
The Cloud
TekaThe Cloud is the AI-native workspace that gives every AI — Claude, ChatGPT, Gemini, and other Frontier & Free or Local models — shared memory and real agents. Not cloud computing: thecloud.so is one workspace where your
Scento Perfume API (MCP server): free fragrance API, no key
ScentoScento Perfume API is a free fragrance API and perfume notes database for developers and AI agents. Search 1,500+ perfumes and 400+ ingredients, compare scents, find similar perfumes, map scent families and build occasio
コメント