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Document Automation MCP Server

by vedantparmar12
GitHub
Python
MIT License
1
  • Linux
  • Apple
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Document Automation

A sophisticated Model Context Protocol (MCP) server that enables AI assistants to automatically analyze codebases and generate comprehensive, professional documentation.

Overview

Document Automation is an intelligent documentation generation system that bridges the gap between AI assistants and code documentation workflows. By implementing the Model Context Protocol (MCP), it allows AI assistants like Claude to seamlessly analyze project structures, extract insights, and generate professional-grade documentation automatically.

Key Features

  • Intelligent Codebase Analysis - Deep project structure and dependency analysis
  • Professional Documentation Generation - Multi-format output (Markdown, HTML, RST, PDF)
  • AI Assistant Integration - Native Claude Desktop and Cursor IDE support
  • Multi-Platform Deployment - Local, Docker, and Google Cloud Run ready
  • Advanced MCP Tools - 5 specialized analysis and generation tools
  • Security-First Design - Built-in validation and safety measures

Architecture

┌─────────────────────────────────────────────────────────────┐ │ AI Assistant (Claude) │ │ ┌─────────────────┐ ┌─────────────────┐ │ │ │ Claude Desktop │ │ Cursor IDE │ │ │ └─────────────────┘ └─────────────────┘ │ └─────────────────────────────┬───────────────────────────────┘ │ MCP Protocol ┌─────────────────────────────▼───────────────────────────────┐ │ Document Automation Server │ │ ┌─────────────┐ ┌─────────────┐ ┌─────────────┐ │ │ │ Analyzers │ │ Generators │ │ Tools │ │ │ │ │ │ │ │ │ │ │ │ • Base │ │ • Standard │ │ • MCP │ │ │ │ • Codebase │ │ • Professional│ │ • Registry │ │ │ └─────────────┘ └─────────────┘ └─────────────┘ │ │ ┌─────────────┐ ┌─────────────┐ │ │ │ Security │ │ Types │ │ │ └─────────────┘ └─────────────┘ │ └─────────────────────────────────────────────────────────────┘

Quick Start

Prerequisites

  • Python 3.8+
  • Git
  • Claude Desktop or Cursor IDE (for AI integration)

Installation

# Clone the repository git clone https://github.com/vedantparmar12/Document-Automation.git cd Document-Automation # Install dependencies pip install -r requirements.txt # Run setup (choose your platform) # Windows setup_claude_desktop.bat # Unix/Linux/macOS chmod +x setup_claude_desktop.sh ./setup_claude_desktop.sh

Basic Usage

Once installed, you can use the system through your AI assistant:

"Analyze the codebase at /path/to/my/project and generate comprehensive documentation"

The system will automatically:

  1. Analyze the project structure and dependencies
  2. Extract key architectural insights
  3. Generate professional documentation
  4. Format the output in your preferred style

MCP Tools Reference

analyze_codebase

Performs comprehensive codebase analysis and structure extraction.

Parameters:

  • path (string): Local folder path or GitHub repository URL
  • source_type (enum): "local" | "github"
  • include_dependencies (boolean): Include dependency analysis (default: true)

Returns:

  • Project structure with file hierarchy
  • Dependencies and versions
  • Codebase metrics and statistics
  • Language distribution analysis

Example:

{ "tool": "analyze_codebase", "parameters": { "path": "https://github.com/user/repo", "source_type": "github", "include_dependencies": true } }

generate_documentation

Creates comprehensive documentation from analyzed codebase.

Parameters:

  • analysis_id (string): ID from previous analyze_codebase call
  • format (enum): "markdown" | "html" | "rst" | "pdf" (default: "markdown")
  • include_api_docs (boolean): Include API documentation (default: true)
  • include_architecture (boolean): Include architecture diagrams (default: true)
  • include_examples (boolean): Include code examples (default: true)

Returns:

  • Formatted documentation content
  • Generation metadata
  • Word count and statistics

list_project_structure

Provides detailed project structure with file information.

Parameters:

  • path (string): Project path
  • source_type (enum): "local" | "github"
  • max_depth (integer): Maximum traversal depth (default: 5)

Returns:

  • Hierarchical file structure
  • File sizes and types
  • Last modification timestamps

extract_api_endpoints

Discovers and documents API endpoints from web frameworks.

Parameters:

  • path (string): Project path
  • source_type (enum): "local" | "github"
  • framework (enum): "auto" | "fastapi" | "flask" | "django" | "express" | "spring"

Returns:

  • List of discovered endpoints
  • HTTP methods and paths
  • Parameter documentation

analyze_dependencies

Analyzes project dependencies and generates dependency documentation.

Parameters:

  • path (string): Project path
  • source_type (enum): "local" | "github"
  • include_dev_dependencies (boolean): Include development dependencies (default: false)

Returns:

  • Dependency list with versions
  • Security analysis
  • Update recommendations

Configuration

Claude Desktop Integration

  1. Configure your claude_desktop_config.json:
{ "mcpServers": { "document-automation": { "command": "python", "args": ["/path/to/Document-Automation/src/main.py"], "env": {} } } }
  1. Restart Claude Desktop

Cursor IDE Integration

  1. Update your MCP settings:
{ "mcpServers": { "document-automation": { "command": "python", "args": ["src/main.py"] } } }
  1. Follow the detailed setup guide in CURSOR_MCP_SETUP_GUIDE.md

Project Structure

Document-Automation/ ├── Configuration │ ├── claude_desktop_config.json # Claude Desktop setup │ ├── cursor_mcp_config.json # Cursor IDE integration │ ├── docker-compose.yml # Container orchestration │ ├── cloudbuild.yaml # Google Cloud Build │ └── requirements.txt # Python dependencies │ ├── Documentation │ ├── README.md # Project overview │ ├── SETUP.md # Installation guide │ ├── DEPLOYMENT.md # Deployment instructions │ ├── MCP_CLIENT_SETUP.md # MCP client setup │ ├── CURSOR_MCP_SETUP_GUIDE.md # Cursor IDE guide │ └── QUICK_SETUP.md # Quick start guide │ ├── Scripts │ ├── setup_claude_desktop.bat # Windows setup │ └── setup_claude_desktop.sh # Unix/Linux setup │ ├── Testing │ ├── test_mcp.py # MCP protocol tests │ └── test_mcp_simple.py # Basic functionality tests │ ├── Source Code │ ├── src/ │ │ ├── main.py # Application entry point │ │ ├── server.py # MCP server implementation │ │ ├── types.py # Data models and schemas │ │ │ │ │ ├── analyzers/ # Analysis components │ │ │ ├── base_analyzer.py # Base analysis framework │ │ │ └── codebase_analyzer.py # Codebase analysis logic │ │ │ │ │ ├── generators/ # Documentation generators │ │ │ ├── documentation_generator.py # Standard documentation │ │ │ └── professional_doc_generator.py # Professional formatting │ │ │ │ │ ├── security/ # Security layer │ │ │ └── validation.py # Input validation & safety │ │ │ │ │ └── tools/ # MCP tools implementation │ │ ├── documentation_tools.py # Tool implementations │ │ └── register_tools.py # Tool registration system │ │ │ └── tests/ # Test suite │ ├── conftest.py # Test configuration │ ├── test_analyzer.py # Analyzer tests │ └── __init__.py │ └── docs/ # Generated documentation └── *.md # Auto-generated analysis reports

Core Components

MCP Server Infrastructure (src/server.py, src/main.py)

  • Purpose: Core MCP protocol implementation
  • Key Features:
    • Tool registration and discovery
    • Request/response handling
    • Error management and validation
    • Async operation support
  • Size: 13.2KB server implementation

Analyzers Module (src/analyzers/)

  • Base Analyzer (base_analyzer.py, 13.9KB)
    • Abstract base class for all analysis operations
    • Common analysis patterns and utilities
    • File system traversal and parsing
  • Codebase Analyzer (codebase_analyzer.py, 7.0KB)
    • Project structure analysis
    • Dependency extraction
    • Language detection and metrics
    • Git repository integration

Documentation Generators (src/generators/)

  • Standard Generator (documentation_generator.py, 15.5KB)
    • Basic documentation generation
    • Multiple output formats support
    • Template-based rendering
  • Professional Generator (professional_doc_generator.py, 37.2KB)
    • Advanced formatting and styling
    • Enterprise-grade documentation
    • Architecture diagrams and visualizations
    • Largest file in the project (1,209 lines)

MCP Tools (src/tools/)

  • Documentation Tools (documentation_tools.py, 18.5KB)
    • Implementation of all MCP tools
    • Tool parameter validation
    • Response formatting
  • Tool Registration (register_tools.py, 2.5KB)
    • Dynamic tool discovery
    • MCP protocol compliance
    • Tool metadata management

Security Layer (src/security/)

  • Validation Module (validation.py, 8.2KB)
    • Input sanitization
    • Path traversal protection
    • Security policy enforcement

Type System (src/types.py, 5.1KB)

  • Pydantic data models
  • Request/response schemas
  • Type safety enforcement

Dependencies

Core Framework Dependencies

  • mcp - Model Context Protocol implementation
  • fastapi - Modern async web framework
  • uvicorn[standard] - ASGI server with standard extensions
  • pydantic - Data validation and serialization

Documentation & Template Engine

  • jinja2 - Template engine for documentation generation
  • markdown - Markdown processing and rendering
  • pygments - Syntax highlighting for code blocks

File & Data Processing

  • aiofiles - Async file operations
  • toml - TOML configuration file parsing
  • PyYAML - YAML file processing
  • python-dotenv - Environment variable management

External Communication

  • httpx - Modern async HTTP client
  • requests - Traditional HTTP library for compatibility
  • gitpython - Git repository analysis and manipulation

Development Support

  • typing-extensions - Extended type hints for better IDE support

Total Dependencies: 15 production packages

Deployment Options

Local Development

# Start the MCP server python src/main.py # Server will be available for MCP clients

Docker Deployment

# Build container docker build -t document-automation . # Run container docker run -p 8000:8000 document-automation # Or use docker-compose docker-compose up

Google Cloud Run

# Deploy to Cloud Run gcloud run deploy document-automation \ --source . \ --platform managed \ --region us-central1

Usage Examples

Basic Codebase Analysis

Ask your AI assistant:

"Analyze the codebase at https://github.com/user/repo"

This triggers:

  1. analyze_codebase tool call
  2. Automatic structure analysis
  3. Dependency extraction
  4. Metrics calculation

Generate Documentation

Ask your AI assistant:

"Generate comprehensive documentation for the analyzed codebase"

This triggers:

  1. generate_documentation tool call
  2. Professional formatting
  3. Multiple output formats
  4. Architecture diagrams

API Endpoint Discovery

Ask your AI assistant:

"Extract all API endpoints from my FastAPI project"

This triggers:

  1. extract_api_endpoints tool call
  2. Framework detection
  3. Endpoint documentation
  4. Parameter extraction

Development & Testing

Testing Framework

  • Unit Tests: tests/test_analyzer.py
  • Integration Tests: test_mcp.py, test_mcp_simple.py
  • Configuration: tests/conftest.py

Running Tests

# Run all tests python -m pytest tests/ # Run MCP integration tests python test_mcp_simple.py # Run with coverage python -m pytest tests/ --cov=src/

Development Setup

# Install in development mode pip install -e . # Install development dependencies pip install -r requirements.txt # Run in development mode python src/main.py

Project Metrics

Scale

  • Total Files: 134
  • Total Lines of Code: 13,231
  • Python Files: 20
  • Documentation Files: 13 markdown files
  • Configuration Files: 5 JSON + 2 YAML files

File Type Distribution

  • Python Files: 20 (core functionality)
  • Markdown Files: 13 (documentation)
  • JSON Files: 5 (configuration)
  • YAML Files: 2 (deployment config)
  • Shell Scripts: 2 (setup automation)

Largest Components

  1. Professional Doc Generator: 1,209 lines (37.2KB)
  2. Documentation Tools: 18.5KB
  3. Documentation Generator: 15.5KB
  4. Base Analyzer: 13.9KB
  5. MCP Server: 13.2KB

Contributing

  1. Fork the repository
  2. Create a feature branch (git checkout -b feature/amazing-feature)
  3. Commit your changes (git commit -m 'Add some amazing feature')
  4. Push to the branch (git push origin feature/amazing-feature)
  5. Open a Pull Request

License

This project is licensed under the MIT License - see the LICENSE file for details.

Support

For questions, issues, or feature requests:

  • Open an issue on GitHub
  • Check the documentation in the docs/ folder
  • Review the setup guides for your specific use case

Acknowledgments

This server cannot be installed

-
security - not tested
A
license - permissive license
-
quality - not tested

How are these scores calculated?

hybrid server

The server is able to function both locally and remotely, depending on the configuration or use case.

A sophisticated server that enables AI assistants to automatically analyze codebases and generate comprehensive, professional documentation.

  1. Overview
    1. Key Features
  2. Architecture
    1. Quick Start
      1. Prerequisites
      2. Installation
      3. Basic Usage
    2. MCP Tools Reference
      1. analyze_codebase
      2. generate_documentation
      3. list_project_structure
      4. extract_api_endpoints
      5. analyze_dependencies
    3. Configuration
      1. Claude Desktop Integration
      2. Cursor IDE Integration
    4. Project Structure
      1. Core Components
        1. MCP Server Infrastructure (src/server.py, src/main.py)
        2. Analyzers Module (src/analyzers/)
        3. Documentation Generators (src/generators/)
        4. MCP Tools (src/tools/)
        5. Security Layer (src/security/)
        6. Type System (src/types.py, 5.1KB)
      2. Dependencies
        1. Core Framework Dependencies
        2. Documentation & Template Engine
        3. File & Data Processing
        4. External Communication
        5. Development Support
      3. Deployment Options
        1. Local Development
        2. Docker Deployment
        3. Google Cloud Run
      4. Usage Examples
        1. Basic Codebase Analysis
        2. Generate Documentation
        3. API Endpoint Discovery
      5. Development & Testing
        1. Testing Framework
        2. Running Tests
        3. Development Setup
      6. Project Metrics
        1. Scale
        2. File Type Distribution
        3. Largest Components
      7. Contributing
        1. License
          1. Support
            1. Acknowledgments

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