mcp server

doc-mcp-server

让AI读懂任何复杂文档 - 解决AI上下文限制问题的通用MCP服务器 | Universal MCP server for AI to understand complex documents

jiahuidegit/doc-mcp-servercommunitylistedunreviewed

Use tool

Set up

Runs on your computer. Pick your agent and operating system.

Before you start

uv (`curl -LsSf https://astral.sh/uv/install.sh | sh`)

~/Library/Application Support/Claude/claude_desktop_config.json

{
  "mcpServers": {
    "jiahuidegit-doc-mcp-server": {
      "command": "uvx",
      "args": [
        "doc-mcp-server"
      ]
    }
  }
}

same format Cursor, GitHub Copilot CLI, Junie, JetBrains AI Assistant, Augment (Auggie CLI), Factory Droid, Kiro, Devin CLI +12

Values in angle brackets are placeholders. Restart Claude Desktop after editing its config.Claude Desktop docs

README

synced Oct 23, 2026

📄 Document Analyzer MCP Server

English | 简体中文

Make AI understand complex documents - MCP server solving AI context limitations

🎯 Key Features

✅ Smart Document Analysis - Auto-detect sections, handle merged cells
✅ Multi-format Support - Excel (.xlsx, .xls) | PDF/Word in development
✅ Precise Field Mapping - Field mapping table + section-level reading
✅ High Performance - Structured caching + lazy loading

🚀 Quick Start

Installation

macOS / Linux (Recommended with pipx)

# Install pipx
brew install pipx  # macOS
# or sudo apt install pipx  # Ubuntu/Debian

# Install doc-mcp-server
pipx install doc-mcp-server

Windows

pip install doc-mcp-server

For more installation options, see Full Installation Guide

Configure Claude Code

Add to ~/.claude.json or your project's config file:

{
  "mcpServers": {
    "document-analyzer": {
      "command": "doc-mcp-server"
    }
  }
}

For detailed configuration, see Quick Start Guide

📚 Full Documentation

Installation Guide - Platform-specific installation steps
Update Guide - How to upgrade to the latest version
Quick Start - Configuration and basic usage
Usage Guide - Complete API and examples
Troubleshooting - Common issues and solutions

💡 Usage Example

# 1. Analyze document structure
analyze_document(file_path="/path/to/document.xlsx")

# 2. Read specific section
read_section(file_path="/path/to/document.xlsx", section_name="Section 1")

# 3. Read single field
read_field(file_path="/path/to/document.xlsx", field_key="Section1_CompanyName")

🛠️ Available Tools

Tool	Description
`analyze_document`	Analyze document structure and generate metadata
`get_structure`	Get cached document structure
`read_field`	Read specific field value
`read_section`	Read entire section data
`write_field`	Write field value (Excel only)
`list_sections`	List all sections
`list_fields`	List all fields
`export_structure`	Export document structure

🎯 Why Use This?

Problem: Large Excel files consume massive tokens when directly read by AI

❌ Traditional: Read entire 323-row Excel → 15000+ tokens → Often fails
✅ Using MCP: Structured reading → 2000 tokens → 90%+ success rate

Performance Improvements:

🚀 Token consumption reduced by 87% (15000 → 2000)
✅ Success rate improved from 30% to 90%+
⚡ Handles 323 rows × 24 columns with 4249 merged cells

🤝 Contributing & Feedback

Report Issues: GitHub Issues
Contribute Code: CONTRIBUTING.md

📄 License

MIT License - see LICENSE for details

Made with ❤️ by Yang Jiahui