What is Mcpify

MCPify is a powerful tool designed to automatically detect APIs in existing projects and convert them into Model Context Protocol (MCP) servers, facilitating integration with AI assistants and MCP-compatible clients.

Use cases

Use cases for MCPify include transforming legacy command-line applications into MCP servers, integrating web APIs with AI assistants, and enhancing existing software projects with minimal changes.

How to use

To use MCPify, install it via pip with the command ‘pip install mcpify’. After installation, you can run the mcpify command to analyze your project and generate an MCP server without modifying the source code.

Key features

Key features of MCPify include automatic API detection, support for CLI tools and web APIs, flexible MCP server management, multiple backend support, configuration validation, parameter detection, and zero code changes required.

Where to use

MCPify can be used in various fields including software development, API integration, and automation, particularly where existing command-line tools or web APIs need to be enhanced with AI capabilities.

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

📦 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 detect /path/to/your/project --output config.json

# OpenAI-powered detection: Most intelligent, requires API key
mcpify openai-detect /path/to/your/project --output config.json

# Camel-AI detection: Advanced agent-based analysis
mcpify camel-detect /path/to/your/project --output config.json

# AST detection: Fast, no API key required
mcpify ast-detect /path/to/your/project --output config.json

2. View and validate the configuration

mcpify view config.json
mcpify validate config.json

3. Start the MCP server

# Method 1: Using mcpify CLI (recommended)
mcpify serve config.json

# Method 2: Direct module invocation
python -m mcpify serve config.json

# HTTP mode for web integration
mcpify serve config.json --mode streamable-http --port 8080

🎯 Detection Strategies

Auto-Detection (Recommended)

The auto-detect command intelligently selects the best available strategy:

mcpify detect /path/to/project

Selection Priority:

1. Camel-AI (if installed) - Most comprehensive analysis
2. OpenAI (if API key available) - Intelligent LLM-based detection
3. AST (always available) - Reliable static analysis fallback

OpenAI Detection 🤖

Uses GPT-4 for intelligent project analysis:

# With API key parameter
mcpify openai-detect /path/to/project --openai-key YOUR_API_KEY

# Using environment variable
export OPENAI_API_KEY="your-api-key"
mcpify openai-detect /path/to/project

Advantages:

• Understands complex code patterns and context
• Generates detailed descriptions and parameter information
• Excellent at identifying non-obvious API endpoints
• Handles multiple programming languages

Camel-AI Detection 🐪

Uses Camel-AI’s ChatAgent framework for comprehensive analysis:

# Install camel-ai first
pip install camel-ai

# Set OpenAI API key (required by Camel-AI)
export OPENAI_API_KEY="your-api-key"

# Run detection
mcpify camel-detect /path/to/project --model-name gpt-4

Advantages:

• Advanced agent-based reasoning
• Deep project structure understanding
• Excellent for complex multi-file projects
• Sophisticated parameter extraction

AST Detection 🔍

Fast, reliable static code analysis:

mcpify ast-detect /path/to/project

Advantages:

• No API key required
• Fast execution
• Reliable for standard patterns (argparse, Flask routes)
• Works offline

📋 Usage Scenarios

For Developers (API Detection & Testing)

# Detect and test your APIs with different strategies
mcpify detect my-project --output my-project.json           # Auto-select best
mcpify openai-detect my-project --output my-project-ai.json # AI-powered
mcpify ast-detect my-project --output my-project-ast.json   # Static analysis

# Compare results
mcpify view my-project.json
mcpify serve my-project.json

For AI-Enhanced Detection

# Use OpenAI for intelligent analysis
export OPENAI_API_KEY="your-key"
mcpify openai-detect complex-project --output smart-config.json

# Use Camel-AI for advanced agent analysis
pip install camel-ai
mcpify camel-detect complex-project --output agent-config.json

For Production Deployment

# Generate configuration with best available strategy
mcpify detect production-app --output prod-config.json

# Deploy as HTTP server
mcpify serve prod-config.json --mode streamable-http --host 0.0.0.0 --port 8080

🔧 Backend Types & Examples

FastAPI/Flask Web Applications

{
  "name": "my-web-api",
  "description": "Web API server",
  "backend": {
    "type": "fastapi",
    "base_url": "http://localhost:8000"
  },
  "tools": [
    {
      "name": "get_user",
      "description": "Get user information",
      "endpoint": "/users/{user_id}",
      "method": "GET",
      "parameters": [
        {
          "name": "user_id",
          "type": "string",
          "description": "User ID"
        }
      ]
    }
  ]
}

Python Modules

{
  "name": "my-python-tools",
  "description": "Python module backend",
  "backend": {
    "type": "python",
    "module_path": "./my_module.py"
  },
  "tools": [
    {
      "name": "calculate",
      "description": "Perform calculation",
      "function": "calculate",
      "parameters": [
        {
          "name": "expression",
          "type": "string",
          "description": "Mathematical expression"
        }
      ]
    }
  ]
}

Command-Line Tools

{
  "name": "my-cli-tool",
  "description": "Command line tool backend",
  "backend": {
    "type": "commandline",
    "config": {
      "command": "python3",
      "args": [
        "./my_script.py"
      ],
      "cwd": "."
    }
  },
  "tools": [
    {
      "name": "process_data",
      "description": "Process data with CLI tool",
      "args": [
        "--process",
        "{input_file}"
      ],
      "parameters": [
        {
          "name": "input_file",
          "type": "string",
          "description": "Input file path"
        }
      ]
    }
  ]
}

⚙️ Detection Configuration

Available Detection Commands

# Auto-detection with strategy selection
mcpify detect <project_path> [--output <file>] [--openai-key <key>]

# Specific detection strategies
mcpify openai-detect <project_path> [--output <file>] [--openai-key <key>]
mcpify camel-detect <project_path> [--output <file>] [--model-name <model>]
mcpify ast-detect <project_path> [--output <file>]

# Configuration management
mcpify view <config_file> [--verbose]
mcpify validate <config_file> [--verbose]
mcpify serve <config_file> [--mode <mode>] [--host <host>] [--port <port>]

Supported Backend Types

• fastapi: FastAPI web applications
• flask: Flask web applications
• python: Python modules and functions
• commandline: Command-line tools and scripts
• external: External programs and services

Server Modes

• stdio: Standard input/output (default MCP mode)
• streamable-http: HTTP Server-Sent Events mode

Parameter Types

• string, integer, number, boolean, array
• Automatic type detection from source code
• Custom validation rules
• Enhanced type inference with AI detection

🚀 Server Configuration

Command Line Options

# Basic usage
mcpify serve config.json

# Specify server mode
mcpify serve config.json --mode stdio              # Default mode
mcpify serve config.json --mode streamable-http    # HTTP mode

# Configure host and port (HTTP mode only)
mcpify serve config.json --mode streamable-http --host localhost --port 8080
mcpify serve config.json --mode streamable-http --host 0.0.0.0 --port 9999

# Real examples with provided configurations
mcpify serve examples/python-server-project/server.json
mcpify serve examples/python-server-project/server.json --mode streamable-http --port 8888
mcpify serve examples/python-cmd-tool/cmd-tool.json --mode stdio

Server Modes Explained

STDIO Mode (Default)

• Uses standard input/output for communication
• Best for local MCP clients and development
• No network configuration needed

mcpify serve config.json
# or explicitly
mcpify serve config.json --mode stdio

Streamable HTTP Mode

• Uses HTTP with Server-Sent Events
• Best for web integration and remote clients
• Requires host and port configuration

# Local development
mcpify serve config.json --mode streamable-http --port 8080

# Production deployment
mcpify serve config.json --mode streamable-http --host 0.0.0.0 --port 8080

📁 Examples

Explore the examples/ directory for ready-to-use configurations:

# Try different detection strategies on examples
mcpify detect examples/python-server-project --output server-auto.json
mcpify openai-detect examples/python-cmd-tool --output cmd-openai.json
mcpify ast-detect examples/python-server-project --output server-ast.json

# View example configurations
mcpify view examples/python-server-project/server.json
mcpify view examples/python-cmd-tool/cmd-tool.json

# Test with examples - STDIO mode (default)
mcpify serve examples/python-server-project/server.json
mcpify serve examples/python-cmd-tool/cmd-tool.json

# Test with examples - HTTP mode
mcpify serve examples/python-server-project/server.json --mode streamable-http --port 8888
mcpify serve examples/python-cmd-tool/cmd-tool.json --mode streamable-http --port 9999

🧪 Development

Running Tests

# Run all tests
python -m pytest tests/ -v

# Run with coverage
python -m pytest tests/ --cov=mcpify --cov-report=html

# Run specific tests
python -m pytest tests/test_detect.py -v

Development Setup

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

# Install optional dependencies for full functionality
pip install openai camel-ai

python -m pytest tests/ -v

Available Commands

MCPify CLI Commands

# Detection commands
mcpify detect <project_path> [--output <file>] [--openai-key <key>]
mcpify openai-detect <project_path> [--output <file>] [--openai-key <key>]
mcpify camel-detect <project_path> [--output <file>] [--model-name <model>]
mcpify ast-detect <project_path> [--output <file>]

# Configuration commands
mcpify view <config_file> [--verbose]
mcpify validate <config_file> [--verbose]

# Server commands
mcpify serve <config_file> [--mode <mode>] [--host <host>] [--port <port>]

🚀 Deployment Options

1. Package Installation

pip install mcpify
# Use mcpify serve for all scenarios

2. Module Invocation

# Run as Python module
python -m mcpify serve config.json
python -m mcpify serve config.json --mode streamable-http --port 8080

3. Docker Deployment

FROM python:3.10-slim
COPY . /app
WORKDIR /app
RUN pip install .
# Optional: Install AI detection dependencies
# RUN pip install openai camel-ai
CMD ["mcpify", "serve", "config.json", "--mode", "streamable-http", "--host", "0.0.0.0", "--port", "8080"]

4. Production HTTP Server

# Start HTTP server for production
mcpify serve config.json --mode streamable-http --host 0.0.0.0 --port 8080

# With custom configuration
mcpify serve config.json --mode streamable-http --host 127.0.0.1 --port 9999

🤝 Contributing

We welcome contributions! Please see our development setup above and:

• Fork the repository
• Create a feature branch
• Add tests for new functionality
• Submit a pull request

Code Quality

# Linting and formatting
ruff check mcpify/
ruff format mcpify/

# Type checking
mypy mcpify/

📄 License

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

🔗 Related Projects

• Model Context Protocol - The protocol specification
• MCP Python SDK - Official Python implementation
• OpenAI API - For AI-powered detection
• Camel-AI - Multi-agent framework for advanced detection

📞 Support

• Documentation: See docs/usage.md for detailed usage instructions
• Examples: Check the examples/ directory for configuration templates
• Issues: GitHub Issues
• Discussions: GitHub Discussions