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Document Automation MCP Server

by vedantparmar12
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Python
Hybrid

📚 Document Automation MCP Server

Python MCP License Code Style

A powerful Model Context Protocol (MCP) server that provides intelligent codebase analysis, comprehensive documentation generation, and multi-format export capabilities. Built for seamless integration with Claude and other AI assistants.

✨ Features

🔍 Intelligent Codebase Analysis

  • AST Parsing: Deep analysis of Python and JavaScript code structures

  • Framework Detection: Automatically identifies frameworks (React, Flask, Django, FastAPI, etc.)

  • Database Schema Analysis: Extracts and visualizes database relationships

  • API Endpoint Discovery: Maps REST endpoints with methods and parameters

  • Security Scanning: Identifies potential vulnerabilities and best practices

  • Dependency Tracking: Analyzes project dependencies and versions

📝 Professional Documentation Generation

  • Multiple Formats: Markdown, HTML, PDF, DOCX, Confluence, Notion, JSON, EPUB

  • Interactive Documentation: Search, navigation, syntax highlighting

  • Mermaid Diagrams: Architecture, database ER, API flows, dependency graphs

  • Multi-language Support: Built-in internationalization

  • Custom Themes: Modern, minimal, dark, corporate, and more

  • Accessibility Compliant: WCAG 2.1 AA standards

🚀 Advanced Capabilities

  • Pagination Support: Handles large repositories efficiently

  • Background Processing: Async analysis for better performance

  • Smart Chunking: Token-aware content splitting

  • Concurrent Analysis: Parallel file processing

  • Export Automation: Batch export to multiple formats

  • Archive Generation: ZIP, TAR, TAR.GZ support

Related MCP server: JSON Resume MCP Server

📋 Table of Contents

🚀 Installation

Prerequisites

  • Python 3.8 or higher

  • Git

  • Claude Desktop or MCP-compatible client

Using pip

# Clone the repository git clone https://github.com/vedantparmar12/Document-Automation.git cd Document-Automation # Install dependencies pip install -r requirements.txt

Using uv (Recommended)

# Install uv (if not already installed) pip install uv # Install dependencies with uv uv pip install -r requirements.txt

🎯 Quick Start

1. Configure Claude Desktop

Add the following to your Claude Desktop configuration file:

Windows: %APPDATA%\Claude\claude_desktop_config.json
macOS: ~/Library/Application Support/Claude/claude_desktop_config.json

{ "mcpServers": { "document-automation": { "command": "python", "args": [ "C:\\path\\to\\Document-Automation\\run_server.py" ] } } }

2. Start the Server

# Using Python directly python run_server.py # Or using uv uv run python run_server.py

3. Use in Claude

Once configured, Claude can analyze codebases and generate documentation:

Analyze this repository: https://github.com/username/repo Generate comprehensive documentation in HTML format

⚙️ Configuration

Environment Variables

Create a .env file in the project root:

# Analysis Settings MAX_FILES=1000 MAX_TOKENS_PER_CHUNK=4000 PAGINATION_STRATEGY=auto # Security Settings ENABLE_SECURITY_SCAN=true CONTENT_FILTERING=true # Export Settings DEFAULT_OUTPUT_DIR=./docs DEFAULT_THEME=modern ENABLE_DIAGRAMS=true

MCP Server Configuration

The server can be configured via wrangler.toml for Cloudflare Workers deployment:

name = "document-automation" compatibility_date = "2024-01-01" [env.production] vars = { ENVIRONMENT = "production" }

📖 Usage Examples

Analyze a Local Codebase

from src.tools.consolidated_documentation_tools import analyze_codebase result = analyze_codebase( path="/path/to/project", source_type="local", include_ast_analysis=True, include_mermaid_diagrams=True, include_security_analysis=True ) print(f"Analysis ID: {result['analysis_id']}") print(f"Total Files: {result['metrics']['total_files']}") print(f"Frameworks: {result['technology_stack']['primary_technologies']}")

Analyze a GitHub Repository

result = analyze_codebase( path="https://github.com/username/repo.git", source_type="github", include_api_endpoints=True, include_database_analysis=True )

Generate Documentation

from src.tools.consolidated_documentation_tools import generate_documentation docs = generate_documentation( analysis_id="analysis_20241212_123456_789", format="interactive", theme="modern", include_toc=True, include_search=True, auto_export_formats=["pdf", "html", "markdown"] ) print(f"Documentation generated: {docs['generated_files']}")

Export to Multiple Formats

from src.tools.consolidated_documentation_tools import export_documentation exports = export_documentation( analysis_id="analysis_20241212_123456_789", formats=["html", "pdf", "docx", "markdown"], theme="corporate", include_diagrams=True, accessibility_compliance=True, archive_formats=["zip"] ) print(f"Exported {len(exports['exported_files'])} files")

🔌 API Reference

Core Tools

analyze_codebase

Comprehensive codebase analysis with built-in features.

Parameters:

  • path (str): Local path or GitHub URL

  • source_type (str): "local" or "github"

  • include_ast_analysis (bool): Enable AST parsing

  • include_framework_detection (bool): Detect frameworks

  • include_database_analysis (bool): Analyze database schemas

  • include_mermaid_diagrams (bool): Generate diagrams

  • include_api_endpoints (bool): Extract API routes

  • include_security_analysis (bool): Security scanning

  • max_files (int): Maximum files to analyze (default: 1000)

  • pagination_strategy (str): "auto", "file_by_file", "chunk_by_chunk", "smart"

Returns:

{ "success": bool, "analysis_id": str, "comprehensive_analysis": { "project_structure": {...}, "dependencies": [...], "api_endpoints": [...], "mermaid_diagrams": {...}, "security_analysis": {...} }, "metrics": {...} }

generate_documentation

Generate professional documentation with all features built-in.

Parameters:

  • analysis_id (str): ID from previous analysis

  • format (str): "markdown", "html", "pdf", "interactive", etc.

  • theme (str): "modern", "minimal", "dark", "corporate", etc.

  • include_toc (bool): Table of contents

  • include_search (bool): Search functionality

  • include_navigation (bool): Navigation sidebar

  • auto_export_formats (list): Additional export formats

Returns:

{ "success": bool, "generated_files": [...], "documentation_stats": {...} }

export_documentation

Export to multiple formats with advanced features.

Parameters:

  • analysis_id (str): ID from previous analysis

  • formats (list): Export formats

  • theme (str): Documentation theme

  • accessibility_compliance (bool): WCAG 2.1 AA compliance

  • archive_formats (list): Archive types ["zip", "tar", "tar.gz"]

📁 Project Structure

Document-Automation/ ├── src/ │ ├── analyzers/ # Codebase analysis modules │ │ ├── base_analyzer.py │ │ ├── codebase_analyzer.py │ │ ├── database_analyzer.py │ │ ├── framework_detector.py │ │ └── project_classifier.py │ ├── diagrams/ # Diagram generation │ │ ├── architecture_diagrams.py │ │ ├── database_diagrams.py │ │ └── mermaid_generator.py │ ├── export/ # Format exporters │ │ └── format_exporter.py │ ├── generators/ # Documentation generators │ │ ├── documentation_generator.py │ │ ├── interactive_doc_generator.py │ │ ├── professional_doc_generator.py │ │ └── readme_template.py │ ├── pagination/ # Pagination and chunking │ │ ├── chunker.py │ │ ├── context.py │ │ ├── strategies.py │ │ └── token_estimator.py │ ├── parsers/ # Language parsers │ │ ├── ast_analyzer.py │ │ ├── javascript_parser.py │ │ ├── python_parser.py │ │ └── mcp_analyzer.py │ ├── processing/ # Background processing │ │ ├── background_processor.py │ │ └── concurrent_analyzer.py │ ├── security/ # Security features │ │ ├── content_filter.py │ │ └── validation.py │ ├── tools/ # MCP tools │ │ └── consolidated_documentation_tools.py │ ├── schemas.py # Data schemas │ └── server.py # MCP server ├── run_server.py # Server entry point ├── requirements.txt # Python dependencies ├── pyproject.toml # Project metadata └── README.md # This file

🏗️ Architecture

System Overview

flowchart TB Client[Claude/MCP Client] --> Server[MCP Server] Server --> Analyzer[Codebase Analyzer] Server --> Generator[Doc Generator] Server --> Exporter[Format Exporter] Analyzer --> AST[AST Parser] Analyzer --> Framework[Framework Detector] Analyzer --> Database[DB Analyzer] Analyzer --> Security[Security Scanner] Generator --> Interactive[Interactive HTML] Generator --> Professional[Professional Docs] Generator --> Diagrams[Mermaid Diagrams] Exporter --> PDF[PDF Export] Exporter --> HTML[HTML Export] Exporter --> Markdown[Markdown Export] Exporter --> DOCX[DOCX Export]

Component Interaction

sequenceDiagram participant Client participant Server participant Analyzer participant Generator participant Exporter Client->>Server: analyze_codebase() Server->>Analyzer: Parse & Analyze Analyzer->>Analyzer: AST, Framework, DB, Security Analyzer-->>Server: Analysis Results Server-->>Client: analysis_id Client->>Server: generate_documentation() Server->>Generator: Create Docs Generator->>Generator: Format, Theme, Diagrams Generator-->>Server: Documentation Server-->>Client: Generated Files Client->>Server: export_documentation() Server->>Exporter: Convert Formats Exporter->>Exporter: PDF, HTML, DOCX Exporter-->>Server: Exported Files Server-->>Client: Export Results

🛠️ Development

Setup Development Environment

# Create virtual environment python -m venv venv source venv/bin/activate # On Windows: venv\Scripts\activate # Install dev dependencies pip install -r requirements.txt pip install black pytest pytest-cov mypy # Run tests pytest tests/ # Format code black src/ # Type checking mypy src/

Running Tests

# Run all tests pytest # Run with coverage pytest --cov=src --cov-report=html # Run specific test file pytest tests/test_analyzer.py

Code Quality

# Format with Black black src/ tests/ # Lint with Flake8 flake8 src/ tests/ # Type check with MyPy mypy src/

🤝 Contributing

Contributions are welcome! Please follow these guidelines:

  1. Fork the repository

  2. Create a feature branch: git checkout -b feature/amazing-feature

  3. Commit your changes: git commit -m 'Add amazing feature'

  4. Push to the branch: git push origin feature/amazing-feature

  5. Open a Pull Request

Contribution Guidelines

  • Follow PEP 8 style guide

  • Add tests for new features

  • Update documentation

  • Ensure all tests pass

  • Keep commits atomic and well-described

📊 Performance

Benchmarks

Operation

Small Repo (<100 files)

Medium Repo (100-1000 files)

Large Repo (>1000 files)

Analysis

~5 seconds

~30 seconds

~2-5 minutes

Doc Generation

~2 seconds

~10 seconds

~30 seconds

Export (all formats)

~3 seconds

~15 seconds

~45 seconds

Optimization Tips

  • Use pagination_strategy="smart" for large repositories

  • Enable include_security_analysis=False if not needed

  • Limit max_files for faster analysis

  • Use concurrent processing for multiple projects

🐛 Troubleshooting

Common Issues

Issue: ModuleNotFoundError: No module named 'src'

# Solution: Add project root to PYTHONPATH export PYTHONPATH="${PYTHONPATH}:/path/to/Document-Automation"

Issue: GitHub rate limiting

# Solution: Set GitHub token export GITHUB_TOKEN="your_token_here"

Issue: Memory errors with large repositories

# Solution: Use pagination analyze_codebase(path="...", max_files=500, pagination_strategy="smart")

📄 License

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

🙏 Acknowledgments

📮 Contact

Vedant Parmar - @vedantparmar12

Project Link: https://github.com/vedantparmar12/Document-Automation

🗺️ Roadmap

  • Support for more programming languages (Go, Rust, Java)

  • Real-time collaboration features

  • Cloud storage integration (S3, GCS)

  • API documentation auto-generation from OpenAPI specs

  • Enhanced security scanning with CVE database

  • Performance profiling and optimization suggestions

  • Custom template support

  • CLI tool for standalone use

⭐ Star History

If you find this project useful, please consider giving it a star! ⭐

Made with ❤️ by Vedant Parmar

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