MCPify - Export all projects as MCP servers!

MCPify is a powerful tool that automatically detects APIs in existing projects and transforms them into Model Context Protocol (MCP) servers. This enables seamless integration of your existing command-line tools, web APIs, and applications with AI assistants and other MCP-compatible clients.

🚀 Features

• Intelligent API Detection: Multiple advanced detection strategies
  • 🤖 OpenAI Detection: Use GPT-4 for intelligent API analysis and tool extraction
  • 🐪 Camel-AI Detection: Leverage Camel-AI's ChatAgent framework for comprehensive analysis
  • 🔍 AST Detection: Static code analysis using Abstract Syntax Trees
  • 🎯 Auto-Selection: Automatically choose the best available detection strategy
• Multiple Project Types: Support for various project architectures
  • CLI Tools: Detect argparse, click, typer-based command-line interfaces
  • Web APIs: Support for Flask, Django, and FastAPI applications with route detection
  • Interactive Commands: Identify command-based interactive applications
  • Python Modules: Extract callable functions and methods
• Flexible MCP Server: Multiple ways to start and control MCP servers
• Multiple Backend Support: Works with command-line tools, HTTP APIs, Python modules, and more
• Configuration Validation: Built-in validation system to ensure correct configurations
• Parameter Detection: Automatically extract route parameters, query parameters, and CLI arguments
• Zero Code Changes: Transform existing projects without modifying their source code
• Professional Architecture: Clean separation between detection, configuration, and server execution

🎨 Interactive UI Features

MCPify now includes a powerful Streamlit-based web interface that makes repository analysis and MCP server configuration generation intuitive and interactive!

🚀 Launch the UI

# Install UI dependencies
pip install 'mcpify[ui]'

# Start the interactive web interface
python -m mcpify.ui

# Or use the convenience function
python -c "from mcpify.ui import start_ui; start_ui()"

Then navigate to http://localhost:8501 in your browser.

✨ Key UI Features

🔍 Repository Analyzer

• GitIngest-style Interface: Clean, intuitive repository input with drag-and-drop support
• Smart Examples: Pre-configured example repositories to try instantly
• Advanced Options: Configurable exclude patterns, file size limits, and detection strategies
• Real-time Progress: Visual progress indicators for each analysis phase
• Multiple Input Types: Support for GitHub URLs, local directories, and Git repositories

🤖 AI-Powered Chat Interface (Coming Soon)

• Conversational API Discovery: Describe what you need in natural language
• Smart Recommendations: AI suggests relevant APIs and tools based on your requirements
• Interactive Configuration: Build MCP configurations through guided conversations
• Context-Aware Suggestions: Leverages repository analysis for targeted recommendations

📊 Intelligent Analysis Workflow

The UI provides a 5-phase intelligent workflow:

1. 📁 Input Phase: Repository selection with examples and advanced options
2. 🔄 Analysis Phase: GitIngest processing with real-time progress tracking
3. 💬 Chat Phase: AI-powered conversation to understand your needs
4. 🎯 Confirmation Phase: Review and confirm detected APIs and tools
5. ✅ Complete Phase: Download configurations and get deployment instructions

🎛️ Advanced Features

• Session Management: Save and restore analysis sessions
• Configuration Validation: Real-time validation with detailed error reporting
• Export Options: Download configurations in multiple formats
• Server Testing: Built-in MCP server testing and validation
• History Tracking: Keep track of all your analysis sessions

🖥️ UI Screenshots & Workflow

Repository Input Interface

┌─────────────────────────────────────────────────────────┐
│  ✨ MCPify ✨                                          │
│  Turn repositories into MCP servers                     │
│                                                         │
│  📁 Repository Input                                    │
│  ┌─────────────────────────────────────┐ ┌───────────┐  │
│  │ https://github.com/user/repo        │ │ 🔍 Analyze│  │
│  └─────────────────────────────────────┘ └───────────┘  │
│                                                         │
│  ⚙️ Advanced Options                                    │
│  • Exclude patterns: *.md, __pycache__/, *.pyc         │
│  • Max file size: 50 KB                                │
│  • Detection strategy: auto                             │
│                                                         │
│  💡 Try these examples:                                 │
│  [FastAPI Todo] [Flask Example] [CLI Tool] [API Client]│
└─────────────────────────────────────────────────────────┘

Analysis Progress Tracking

┌─────────────────────────────────────────────────────────┐
│  🔄 Analysis Progress                                   │
│  ████████████████████░░░░ 80%                          │
│                                                         │
│  Validating Configuration                               │
│  Checking configuration validity...                     │
│                                                         │
│  ✅ GitIngest  ✅ Detect APIs  🔄 Validate  ⏳ Complete │
└─────────────────────────────────────────────────────────┘

Results Dashboard

┌─────────────────────────────────────────────────────────┐
│  ✅ Analysis Complete                                   │
│                                                         │
│  📊 Repository: my-fastapi-app    🗂️ Files: 45         │
│  🐍 Language: Python             ⚡ Framework: FastAPI  │
│  ⏱️ Time: 12.3s                  📁 Analyzed: 32       │
│                                                         │
│  📋 Summary | ⚙️ Configuration | 📊 Validation | 📝 Code│
│                                                         │
│  🎉 Generated 8 API tools with FastAPI backend         │
│  📥 Download Configuration                              │
└─────────────────────────────────────────────────────────┘

🎯 UI Usage Examples

Quick Repository Analysis

# Start the UI
python -m mcpify.ui

# In the browser:
# 1. Enter: https://github.com/tiangolo/fastapi
# 2. Click "🔍 Analyze"
# 3. Wait for analysis completion
# 4. Download the generated configuration

Advanced Configuration

# Start UI with custom settings
python -m mcpify.ui

# Configure advanced options:
# • Exclude patterns: "*.md, tests/, docs/"
# • Max file size: 100 KB
# • Detection strategy: openai
# • Include private repos: Yes

🔧 UI Configuration

The UI can be customized through environment variables:

# Custom port
export STREAMLIT_SERVER_PORT=8502
python -m mcpify.ui

# Custom host
export STREAMLIT_SERVER_ADDRESS=0.0.0.0
python -m mcpify.ui

# Enable debug mode
export STREAMLIT_LOGGER_LEVEL=debug
python -m mcpify.ui

🎨 UI Architecture

mcpify/ui/
├── __init__.py           # UI module exports
├── main.py              # UI entry point
├── app.py               # Main Streamlit application
├── models.py            # Data models for UI
├── session_manager.py   # Session and history management
├── components/          # Reusable UI components
│   ├── __init__.py
│   ├── chat_interface.py      # AI chat components
│   ├── sidebar.py             # Navigation sidebar
│   └── detection_results.py   # Results display
└── pages/               # Individual page implementations
    ├── __init__.py
    └── repository_analyzer.py # Main analyzer page

🚀 UI Development

Want to contribute to the UI? Here's how to get started:

# Install UI development dependencies
pip install 'mcpify[ui,dev]'

# Run the UI in development mode
streamlit run mcpify/ui/app.py --server.runOnSave true

# Run UI tests
python -m pytest tests/test_ui_*.py -v

📦 Installation

Using pip (recommended)

pip install mcpify

From source

git clone https://github.com/your-username/mcpify.git
cd mcpify
pip install -e .

Optional Dependencies

For enhanced detection capabilities:

# For OpenAI-powered detection
pip install openai
export OPENAI_API_KEY="your-api-key"

# For Camel-AI powered detection
pip install camel-ai

🏗️ Project Architecture

mcpify/
├── mcpify/                    # Core package
│   ├── cli.py                 # CLI interface with detection commands
│   ├── __main__.py            # Module entry point
│   ├── wrapper.py             # MCP protocol wrapper
│   ├── backend.py             # Backend adapters
│   ├── detect/                # Detection module
│   │   ├── __init__.py        # Module exports
│   │   ├── base.py            # Base detector class
│   │   ├── ast.py             # AST-based detection
│   │   ├── openai.py          # OpenAI-powered detection
│   │   ├── camel.py           # Camel-AI detection
│   │   ├── factory.py         # Detector factory
│   │   └── types.py           # Type definitions
│   └── validate.py            # Configuration validation
├── examples/                  # Example projects
├── docs/                      # Documentation
└── tests/                     # Test suite

🛠️ Quick Start

1. Intelligent API Detection

MCPify offers multiple detection strategies. Use the best one for your needs:

# Auto-detection (recommended): Automatically selects the best available strategy
mcpify