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angrysky56

mcp-creator-mcp

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1 stars PythonOthers Updated Oct 3, 2025

Documentation

MCP-Creator-MCP 🚀

A meta-MCP server that democratizes MCP server creation through AI-guided workflows and intelligent templates.

Transform vague ideas into production-ready MCP servers with minimal cognitive overhead and maximum structural elegance.

🎯 Vision

Creating MCP servers should be as simple as describing what you want. MCP Creator bridges the gap between idea and implementation, providing intelligent guidance, proven templates, and streamlined workflows.

✨ Core Features

  • 🤖 AI-Guided Creation: Get intelligent suggestions and best practices tailored to your use case
  • 📚 Template Library: Curated collection of proven MCP server patterns
  • 🔄 Workflow Engine: Save and reuse creation workflows for consistent results
  • 🎨 Gradio Interface: User-friendly web interface for visual server management
  • 🔧 Multi-Language Support: Python, Gradio, and expanding language ecosystem
  • 📊 Built-in Monitoring: Server health checks and operational visibility
  • 🛡️ Best Practices: Automated validation and security recommendations
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🚀 Quick Start

Prerequisites

  • Python 3.10 or higher
  • uv package manager
  • Claude Desktop (for MCP integration)

Installation

bash
# Clone and set up the project
git clone https://github.com/angrysky56/mcp-creator-mcp.git
cd mcp-creator-mcp

# Create and activate virtual environment
uv venv --python 3.12 --seed
source .venv/bin/activate

# Install dependencies
uv add -e .

# Configure environment
cp .env.example .env
# Edit .env with your API keys (see Configuration section)

Basic Usage

1. Configure Claude Desktop:

bash
# Copy the example config
   cp example_mcp_config.json ~/path/to/claude_desktop_config.json
   # Edit paths and API keys as needed

2. Start using in Claude Desktop:

    Option 2: Standalone Interface

    bash
    # Launch the Gradio interface
    uv run gradio_interface.py
    
    # Or use the CLI
    uv run mcp-creator-gui

    📖 Configuration

    Environment Variables

    Create a `.env` file with your settings:

    env
    # AI Model Providers (at least one required for AI guidance)
    ANTHROPIC_API_KEY=your_anthropic_key_here
    OPENAI_API_KEY=your_openai_key_here
    OLLAMA_BASE_URL=http://localhost:11434
    
    # MCP Creator Settings
    DEFAULT_OUTPUT_DIR=./mcp_servers
    LOG_LEVEL=INFO
    
    # Gradio Interface
    GRADIO_SERVER_PORT=7860
    GRADIO_SHARE=false

    Claude Desktop Integration

    1. Edit your Claude Desktop config (usually at `~/.config/Claude/claude_desktop_config.json`):

    json
    {
      "mcpServers": {
        "mcp-creator": {
          "command": "uv",
          "args": [
            "--directory",
            "/path/to/mcp-creator-mcp",
            "run",
            "python",
            "main.py"
          ],
          "env": {
            "ANTHROPIC_API_KEY": "your_key_here"
          }
        }
      }
    }

    2. Restart Claude Desktop

    🛠️ Usage Examples

    Creating Your First MCP Server

    python
    # In Claude Desktop, ask:
    "Create an MCP server called 'weather_helper' that provides weather data and forecasts"
    
    # Or use the tool directly:
    create_mcp_server(
        name="weather_helper",
        description="Provides weather data and forecasts",
        language="python",
        template_type="basic",
        features=["tools", "resources"]
    )

    Getting AI Guidance

    python
    # Ask for specific guidance:
    get_ai_guidance(
        topic="security",
        server_type="database"
    )
    
    # Or access guidance resources:
    # Use resource: mcp-creator://guidance/sampling

    Managing Templates

    python
    # List available templates
    list_templates()
    
    # Filter by language
    list_templates(language="python")

    🏗️ Architecture

    Core Principles

    • Simplicity: Each component has a single, clear responsibility
    • Predictability: Consistent patterns reduce cognitive load
    • Extensibility: Modular design enables easy customization
    • Reliability: Comprehensive error handling and graceful degradation

    Component Overview

    code
    ├── src/mcp_creator/
    │   ├── core/              # Core server functionality
    │   │   ├── config.py      # Clean configuration management
    │   │   ├── template_manager.py  # Template system
    │   │   └── server_generator.py # Server creation engine
    │   ├── workflows/         # Workflow management
    │   ├── ai_guidance/       # AI assistance system
    │   └── utils/             # Shared utilities
    ├── templates/             # Template library
    ├── ai_guidance/           # Guidance content
    └── mcp_servers/          # Generated servers (default)

    📚 Template System

    Available Templates

    • Python Basic: Clean, well-structured foundation
    • Python with Resources: Database and API integration patterns
    • Python with Sampling: AI-enhanced server capabilities
    • Gradio Interface: Interactive UI with MCP integration

    Creating Custom Templates

    Templates use Jinja2 with clean abstractions:

    python
    # Template structure
    templates/languages/{language}/{template_name}/
    ├── metadata.json          # Template configuration
    ├── template.py.j2        # Main template file
    └── README.md.j2          # Documentation template

    🔄 Workflow System

    Saving Workflows

    python
    save_workflow(
        name="Database MCP Server",
        description="Complete database integration workflow",
        steps=[
            {
                "id": "collect_requirements",
                "type": "input",
                "config": {"fields": ["db_type", "connection_string"]}
            },
            {
                "id": "security_review",
                "type": "ai_guidance",
                "config": {"topic": "database_security"}
            },
            {
                "id": "generate_server",
                "type": "generation",
                "config": {"template": "python:database"}
            }
        ]
    )

    🔧 Development

    Project Structure

    The codebase follows clean architecture principles:

    • Separation of Concerns: Each module has a single responsibility
    • Dependency Injection: Components are loosely coupled
    • Error Boundaries: Graceful failure handling throughout
    • Type Safety: Comprehensive type hints and validation

    Adding New Templates

    1. Create template directory: `templates/languages/{lang}/{name}/`

    2. Add `metadata.json` with template configuration

    3. Create `template.{ext}.j2` with Jinja2 template

    4. Test with the template manager

    Contributing

    1. Fork the repository

    2. Create a feature branch with descriptive name

    3. Follow the existing code patterns and style

    4. Add tests for new functionality

    5. Submit a pull request with clear description

    🛡️ Security & Best Practices

    Built-in Protections

    • Input Validation: All user inputs are validated and sanitized
    • Process Management: Proper cleanup prevents resource leaks
    • Error Handling: Graceful failure with helpful messages
    • Logging: Comprehensive operational visibility
    • Use environment variables for sensitive data
    • Implement rate limiting for production deployments
    • Regular security audits of generated servers
    • Monitor server performance and resource usage

    🐛 Troubleshooting

    Common Issues

    Server won't start:

    bash
    # Check dependencies
    uv add -e .
    
    # Verify configuration
    cat .env
    
    # Check logs
    tail -f logs/mcp-creator.log

    Claude Desktop integration:

    bash
    # Verify config file syntax
    python -m json.tool claude_desktop_config.json
    
    # Check server connectivity
    python main.py --test

    Template errors:

    bash
    # List available templates
    uv run python -c "from src.mcp_creator import TemplateManager; print(TemplateManager().list_templates())"

    📊 Monitoring & Operations

    Health Checks

    The server provides built-in health monitoring:

    • Resource usage tracking
    • Error rate monitoring
    • Performance metrics
    • Template validation

    Logging

    All operations are logged to stderr (MCP compliance):

    bash
    # View logs in real-time
    python main.py 2>&1 | tee mcp-creator.log

    🚀 What's Next?

    • Multi-language expansion: TypeScript, Go, Rust templates
    • Cloud deployment: Integration with major cloud platforms
    • Collaboration features: Team workflows and template sharing
    • Advanced AI: Enhanced code generation and optimization
    • Marketplace: Community template and workflow ecosystem

    📝 License

    MIT License - see LICENSE for details.

    🤝 Contributing

    We welcome contributions! Please see CONTRIBUTING.md for guidelines.

    💬 Support


    Built with ❤️ for the MCP community

    *MCP Creator makes sophisticated AI integrations accessible to everyone, from hobbyists to enterprise teams.*

    Frequently asked questions

    What is mcp-creator-mcp?

    mcp-creator-mcp is a Model Context Protocol (MCP) server listed in the TrackMCP directory.

    How do I install mcp-creator-mcp?

    Open the GitHub repository and follow its README. Most MCP servers are added to your client's MCP config, then called by your agent.

    Is mcp-creator-mcp open source?

    Yes — it is hosted on GitHub at https://github.com/angrysky56/mcp-creator-mcp and has 1 stars.

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