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:

Camel-AI (if installed) - Most comprehensive analysis
OpenAI (if API key available) - Intelligent LLM-based detection
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

Name		Name	Last commit message	Last commit date
Latest commit History 16 Commits
.github/workflows		.github/workflows
docs		docs
examples		examples
mcpify		mcpify
tests		tests
.gitignore		.gitignore
.pre-commit-config.yaml		.pre-commit-config.yaml
LICENSE		LICENSE
README.md		README.md
pyproject.toml		pyproject.toml

License

camel-ai/mcpify

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MCPify - Export all projects as MCP servers!

🚀 Features

📦 Installation

Using pip (recommended)

From source

Optional Dependencies

🏗️ Project Architecture

🛠️ Quick Start

1. Intelligent API Detection

2. View and validate the configuration

3. Start the MCP server

🎯 Detection Strategies

Auto-Detection (Recommended)

OpenAI Detection 🤖

Camel-AI Detection 🐪

AST Detection 🔍

📋 Usage Scenarios

For Developers (API Detection & Testing)

For AI-Enhanced Detection

For Production Deployment

🔧 Backend Types & Examples

FastAPI/Flask Web Applications

Python Modules

Command-Line Tools

⚙️ Detection Configuration

Available Detection Commands

Supported Backend Types

Server Modes

Parameter Types

🚀 Server Configuration

Command Line Options

Server Modes Explained

STDIO Mode (Default)

Streamable HTTP Mode

📁 Examples

🧪 Development

Running Tests

Development Setup

Available Commands

MCPify CLI Commands

🚀 Deployment Options

1. Package Installation

2. Module Invocation

3. Docker Deployment

4. Production HTTP Server

🤝 Contributing

Code Quality

📄 License

🔗 Related Projects

📞 Support

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