Skip to main content
Glama
Sign In
mcp-docparser

mcp-docparser

by Jasonzld
OverviewSchemaSourceRelated ServersDiscussionsAdmin
JavaScript
Local

🚀 MCP Document Parser - Portable Edition

一键部署文档解析工具 for Claude Desktop & CLI

📥 快速安装

Windows

install.bat

macOS/Linux

chmod +x install.sh
./install.sh

✨ 功能特性

  • 📄 PDF 文档解析

  • 📝 Word (docx/doc) 提取

  • 📊 Excel (xlsx/xls) 表格读取

  • 🖼️ 图片OCR (JPG/PNG/BMP等，支持中英文)

  • 📑 Markdown/HTML/CSV/JSON 支持

  • 🔍 文档分块处理

  • 📋 元数据提取

💾 资源占用

对比项

MCP Parser

RAGFlow

内存

~200MB

4GB+

CPU

<5%

50-100%

依赖

Node.js

Docker+WSL2

启动

即时

2-3分钟

🔧 系统要求

  • Node.js 16+ (安装器会自动安装)

  • 支持系统: Windows/macOS/Linux

  • Claude Desktop 或 Claude CLI

📦 包含文件

mcp-docparser-portable/
├── install.bat          # Windows安装器
├── install.sh          # macOS/Linux安装器
├── package.json        # 项目配置
├── server.js          # MCP服务器
└── README.md          # 本文档

🎯 使用方法

安装完成后，在Claude中使用以下MCP工具：

1. 解析文档

使用MCP工具 parse_document 解析 "C:/path/to/document.pdf"

2. 图片OCR

使用MCP工具 ocr_image 识别 "C:/path/to/image.jpg" 中的文字

3. 提取文本

使用MCP工具 extract_text 从 "C:/path/to/document.docx" 提取文本

4. 文档分块

使用MCP工具 chunk_document 将 "C:/path/to/large.pdf" 分成1000字符的块

5. 获取文档信息

使用MCP工具 get_document_info 获取 "C:/path/to/file.xlsx" 的信息

🛠️ 手动配置

如果自动配置失败，手动添加到配置文件：

Claude Desktop (Windows)

路径: %APPDATA%\Claude\claude_desktop_config.json

Claude Desktop (macOS)

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

Claude CLI (所有平台)

路径: ~/.claude/claude_desktop_config.json

配置内容：

{
  "mcpServers": {
    "docparser": {
      "command": "node",
      "args": ["安装路径/server.js"]
    }
  }
}

🔍 故障排除

  1. Node.js未安装

    • Windows: 安装器会自动下载安装

    • macOS: 使用 brew install node

    • Linux: 使用 apt install nodejs 或 yum install nodejs

  2. 权限问题

    • Windows: 以管理员身份运行

    • macOS/Linux: 使用 sudo

  3. Claude未识别MCP

    • 重启Claude Desktop/CLI

    • 检查配置文件路径是否正确

📊 性能对比

功能

速度

准确率

PDF解析

<1秒/页

99%

图片OCR

2-3秒/图

95%+

Word提取

<1秒

100%

Excel读取

<2秒

100%

🌟 优势

  1. 轻量级 - 无需Docker/WSL2

  2. 跨平台 - Windows/macOS/Linux通用

  3. 易部署 - 一键安装脚本

  4. 高性能 - 本地处理，无网络延迟

  5. 全功能 - 支持所有常见文档格式

📝 许可

MIT License - 免费使用和分发

🤝 贡献

欢迎提交Issue和Pull Request

让Claude拥有强大的文档处理能力，只需200MB内存！

Available Tools

7 tools
analyze_imageC

Analyze image content, detect text presence, and get metadata

Inputs: image_path (required).

batch_ocrC

Process multiple images in a folder and extract text

Inputs: folder_path (required).

chunk_documentC

Split document or extracted text into chunks for processing

Inputs: file_path (required), overlap, chunk_size.

extract_textB

Extract text from any document or image using OCR if needed

Inputs: file_path (required).

get_document_infoC

Get metadata and structure information about any document or image

Inputs: file_path (required).

ocr_imageC

Extract text from image using OCR (supports Chinese and English)

Inputs: image_path (required), language.

parse_documentC

Parse any document including images with OCR (PDF, Word, Excel, Images, etc.)

Inputs: file_path (required).

Tool Schema Changelog

Recent tool additions, removals, and schema changes observed during successful MCP inspections.

    Deploy Server
    One endpoint for every MCP toolRayrun runs every MCP server behind one URL and holds the credentials. Each call is decided per client, tool, and argument, then recorded.ray.runAd

    TDQS

    B3.1/5.0

    Scored across 7 tools

    Disambiguation2/5

    Multiple tools have overlapping purposes: parse_document, extract_text, and ocr_image all extract text from images/documents, with descriptions that are nearly indistinguishable. analyze_image also overlaps with get_document_info for image metadata, making it hard for an agent to choose the right tool.

    Naming Consistency4/5

    All tool names use snake_case and most follow a verb_noun pattern (e.g., analyze_image, chunk_document, get_document_info). batch_ocr and ocr_image deviate slightly by starting with a noun or acronym, but the overall convention is clear and readable.

    Tool Count5/5

    Seven tools is well-scoped for a document parsing server, providing distinct capabilities for parsing, OCR, text extraction, metadata, image analysis, and chunking without being overwhelming or thin.

    Completeness4/5

    The surface covers core document parsing tasks: parsing, text extraction, OCR (including batch and language support), metadata retrieval, image analysis, and chunking. Minor gaps exist, such as tools for output saving or format-specific handling, but agents can work around them.

    Maintenance

    ActivityInactive
    ResponsivenessNo recent community issues

    Resources

    Related MCP Connectors

    Related MCP Servers

    • vectorise-mcp

      jameslovespancakes
      A
      license
      Not graded
      quality
      D
      maintenance
      Local MCP server that indexes folders of documents into a hybrid vector + keyword search index for Claude Desktop, with support for PDFs, Office files, and images via OCR.
      MIT

    • Techtenstein PDF MCP

      sathvic-kollu
      A
      license
      Not graded
      quality
      D
      maintenance
      MCP server that gives your Claude, Cline, or Cursor session the ability to extract text, tables, and metadata from any PDF URL — including scanned PDFs via OCR.
      MIT