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:

📁 Input Phase: Repository selection with examples and advanced options
🔄 Analysis Phase: GitIngest processing with real-time progress tracking
💬 Chat Phase: AI-powered conversation to understand your needs
🎯 Confirmation Phase: Review and confirm detected APIs and tools
✅ 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 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 36 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

🎨 Interactive UI Features

🚀 Launch the UI

✨ Key UI Features

🔍 Repository Analyzer

🤖 AI-Powered Chat Interface (Coming Soon)

📊 Intelligent Analysis Workflow

🎛️ Advanced Features

🖥️ UI Screenshots & Workflow

Repository Input Interface

Analysis Progress Tracking

Results Dashboard

🎯 UI Usage Examples

Quick Repository Analysis

Advanced Configuration

🔧 UI Configuration

🎨 UI Architecture

🚀 UI Development

📦 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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