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mcp-qdrant-docs MCP Server

@kazuph

代码仓库

基本信息

分类

数据库

传输方式

stdio

作者

kazuph

关于 mcp-qdrant-docs MCP Server

An MCP server that scrapes websites, indexes content into Qdrant, and provides a query tool.

mcp-qdrant-docs MCP Server 配置

使用下面的配置,将此服务器添加到你的 MCP 客户端。

{
  "mcpServers": {
    "mcp-qdrant-docs": {
      "command": "npx",
      "args": [
        "@kazuph/mcp-qdrant-docs",
        "--start-url",
        "https://example-docs.com/",
        "--collection-name",
        "my-docs",
        "--limit",
        "50",
        "--debug"
      ]
    }
  }
}

mcp-qdrant-docs MCP Server 工具

工具列表暂未提供

当前收录暂时没有可展示的工具列表，不代表服务没有工具。请查阅项目文档了解支持的能力。

点击上方按钮,在临时沙箱中运行此服务器并实时获取工具列表。

概览

What is mcp-qdrant-docs MCP Server?

A TypeScript-based MCP server that scrapes website content, indexes it into a Qdrant vector database, and provides a tool to answer natural language questions about the indexed content. Designed for developers who want to query documentation or web pages using an AI assistant.

How to use mcp-qdrant-docs MCP Server?

Install globally with npm i -g @kazuph/mcp-qdrant-docs or use npx in your MCP client configuration. Configure in Claude Desktop’s claude_desktop_config.json by setting command, args, and environment variables such as DOCS_URL, QDRANT_URL, and COLLECTION_NAME. Then invoke the tool (e.g., ask_reactrouter_docs) with a natural language query.

Key features of mcp-qdrant-docs MCP Server

  • Scrapes websites and indexes content into Qdrant.
  • Dynamically generates a tool per website (e.g., ask_reactrouter_docs).
  • Embeds queries and searches Qdrant for relevant chunks.
  • Supports force reindexing and limits on pages scraped.
  • Configurable via CLI options and environment variables.

Use cases of mcp-qdrant-docs MCP Server

  • Ask questions about React Router documentation using natural language.
  • Query Hono.dev documentation content directly.
  • Interrogate Gradio.app LLMs documentation.
  • General Q&A over any website’s documentation.

常见问题

1

What does the server do with scraped content?

It processes, chunks, and embeds the content using a sentence transformer model, then stores embeddings and chunks in a Qdrant collection specific to the website.

2

How do I configure the server for a new documentation site?

Set the `DOCS_URL` environment variable (or `--start-url` CLI option) and optionally `QDRANT_URL`, `COLLECTION_NAME`, and `EMBEDDING_MODEL`. Add an entry to your MCP client configuration file.

3

What are the dependencies/runtime requirements?

Requires Node.js and npm. The server needs access to a running Qdrant instance (default `http://localhost:6333`) and uses sentence transformer models from Hugging Face.

4

Where does the data live?

Scraped content and embeddings are stored in a Qdrant collection whose name is based on `COLLECTION_NAME` and the sanitized hostname of the scraped site.

5

What is the transport/auth method?

The server communicates via MCP protocol over stdio. No authentication is mentioned in the README.

评论

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