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mcp-docs-reader

@AIMIZING

代码仓库

关于 mcp-docs-reader

A lightweight MCP server that loads PDF files, extracts and chunks their text, builds a semantic vector index, and returns relevant passages to Claude or other AI agents for document-based question answering.

基本信息

分类

记忆与知识

运行时

python

传输方式

stdio

发布者

AIMIZING

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_docs_reader": {
      "command": "uv",
      "args": [
        "venv"
      ]
    }
  }
}

工具

未检测到工具

工具是从 README 中自动提取的。维护者可以在 ## Tools 标题下列出工具,即可填充这部分内容。

概览

What is mcp-docs-reader?

mcp-docs-reader is a lightweight MCP server that loads PDF files from a local folder, extracts and chunks their content, builds a semantic search index, and sends relevant passages to Claude Desktop for document-based question answering. It is intended for use with Claude’s MCP desktop feature.

How to use mcp-docs-reader?

Download or clone the project, set up the uv virtual environment (manually or via setup.bat), and install dependencies. Then edit your Claude Desktop claude_desktop_config.json to include the content from docReader_config.json, replacing the placeholder path with the actual project path. Launch Claude Desktop; it will automatically connect to the MCP tool.

Key features of mcp-docs-reader

  • Loads and processes PDF documents from a local docs/ folder
  • Extracts text and splits it into semantic chunks
  • Generates vector embeddings using SentenceTransformer
  • Builds a FAISS-based vector index for semantic search
  • Retrieves top-k relevant chunks based on user query
  • Constructs a prompt with passages and returns it to Claude

Use cases of mcp-docs-reader

  • Ask Claude questions based on your local PDF documents.
  • Summarize key points from registered file contents.

FAQ from mcp-docs-reader

What is mcp-docs-reader intended for?

It is intended for use with Claude Desktop’s MCP feature to enable document-based question answering from local PDF files.

What kind of documents does it support?

It supports PDF files located in a local docs/ folder.

How are documents processed?

Text is extracted, split into semantic chunks, embedded with SentenceTransformer, and indexed with FAISS for semantic search.

What does mcp-docs-reader return to Claude?

It constructs and returns a prompt containing top-k relevant passages along with the user’s question.

评论

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