Deploys the MCP server on Cloudflare's edge network for global distribution, leveraging Workers, D1 Database, R2 Storage, and Durable Objects
Extracts API endpoints and structure from Django web applications
Analyzes Express.js applications to document API endpoints and routes
Detects and documents API endpoints from FastAPI projects
Discovers and documents API endpoints from Flask applications
Integrates with Git repositories for project analysis and documentation generation
Allows analysis of GitHub repositories to extract project structure, dependencies, and codebase metrics
Discovers and documents API endpoints from Spring framework applications
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
Quick Start
Prerequisites
Python 3.8+
Git
Claude Desktop or Cursor IDE (for AI integration)
Installation
Basic Usage
Once installed, you can use the system through your AI assistant:
The system will automatically:
Analyze the project structure and dependencies
Extract key architectural insights
Generate professional documentation
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 URLsource_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:
generate_documentation
Creates comprehensive documentation from analyzed codebase.
Parameters:
analysis_id
(string): ID from previous analyze_codebase callformat
(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 pathsource_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 pathsource_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 pathsource_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
Configure your
Restart Claude Desktop
Cursor IDE Integration
Update your MCP settings:
Follow the detailed setup guide in
Project Structure
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
Docker Deployment
Google Cloud Run
Usage Examples
Basic Codebase Analysis
Ask your AI assistant:
This triggers:
analyze_codebase tool call
Automatic structure analysis
Dependency extraction
Metrics calculation
Generate Documentation
Ask your AI assistant:
This triggers:
generate_documentation tool call
Professional formatting
Multiple output formats
Architecture diagrams
API Endpoint Discovery
Ask your AI assistant:
This triggers:
extract_api_endpoints tool call
Framework detection
Endpoint documentation
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
Development Setup
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
Professional Doc Generator: 1,209 lines (37.2KB)
Documentation Tools: 18.5KB
Documentation Generator: 15.5KB
Base Analyzer: 13.9KB
MCP Server: 13.2KB
Contributing
Fork the repository
Create a feature branch (
git checkout -b feature/amazing-feature
)Commit your changes (
git commit -m 'Add some amazing feature'
)Push to the branch (
git push origin feature/amazing-feature
)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/
folderReview the setup guides for your specific use case
Acknowledgments
Built on the Model Context Protocol (MCP)
Integrates with Claude and Cursor IDE
Uses modern Python async frameworks for optimal performance
This server cannot be installed
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.
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