Code MCP Server

🚀 MCP Server

A server implementation based on the Model-Controller-Provider (MCP) architecture using FastAPI, which solves the problem of efficient API development and management, and provides a high - performance and easy - to - use server solution.

🚀 Quick Start

1. Clone the repository:

git clone [Repository URL]
cd mcp-server

2. Create and activate a Conda environment:

conda create -n mcp_env python=3.8
conda activate mcp_env

3. Install dependencies:

pip install -r requirements.txt

4. Start the server:

python main.py

The server will start at http://localhost:8000.

✨ Features

• FastAPI - based REST API: High - performance, easy to use, and fast to code.
• MCP Architecture:
  • Model: Data structures and database schemas.
  • Controller: Business logic and request handlers.
  • Provider: Data access and external service integration.
• Modern Technology Stack:
  • SQLAlchemy for ORM.
  • Pydantic for data validation.
  • FastAPI for the API framework.
  • SQLite for the development database.
• API Features:
  • Automatic OpenAPI documentation.
  • Request validation.
  • Response serialization.
  • CORS middleware.
  • Type checking.

📦 Installation

1. Create a Conda environment:

# Create a new environment named codeMcpServer with Python 3.12
conda create -n codeMcpServer python=3.12
# Activate the environment
conda activate codeMcpServer

2. Install dependencies:

# Use the provided environment file (recommended)
conda env create -f environment.yml

# Or use pip
pip install -r requirements.txt

# Or use conda with the conda - forge channel
conda config --add channels conda - forge
conda install --file requirements.txt

3. Run the server:

uvicorn main:app --reload

The server will start at http://localhost:8000.

💻 Usage Examples

Basic Usage

# Start the server
uvicorn main:app --reload

Advanced Usage

# Start the server with custom host and port
uvicorn main:app --host 0.0.0.0 --port 8080

📚 Documentation

The complete API documentation can be found at the following links:

• Swagger UI: http://localhost:8000/docs
• ReDoc: http://localhost:8000/redoc

Please refer to the docs directory in the project for the full API documentation and implementation details.

🔧 Technical Details

Project Structure

.
├── api/              # API routes and endpoints
│   └── v1/          # API version 1
│       ├── __init__.py
│       └── items.py # Item endpoints
├── controllers/      # Business logic and request handlers
│   └── item_controller.py
├── providers/       # Data access and external service integration
│   └── item_provider.py
├── models/         # Data models and database schemas
│   └── item.py
├── core/          # Core configuration and tools
│   ├── config.py  # Application configuration
│   └── database.py # Database configuration
├── main.py        # Application entry point
├── requirements.txt # Project dependencies
├── environment.yml  # Conda environment file
└── README.md      # Project documentation

The project follows a standard Python package structure:

.
├── src/           # Core code
│   ├── MCP/
│   │   ├── controllers/     # Request handling logic
│   │   ├── providers/       # Data access logic
│   │   └── models/          # Data model definitions
│   ├── config.py            # Application configuration
│   └── database.py          # Database connection configuration
├── main.py        # Application entry point
├── requirements.txt # Project dependencies
└── README.md      # Project documentation

Dependencies

The key dependencies in the project are:

• Python 3.12.9
• FastAPI 0.112.2
• SQLAlchemy 2.0.37
• Pydantic 2.11.2
• Uvicorn 0.32.1
• Python - dotenv 0.21.0

For the full list of dependencies, please refer to environment.yml or requirements.txt.

Environment Management

Exporting the Environment

To export the current environment for sharing:

# Export the full environment (without prefix) for exact versions
conda env export | grep -v '^prefix' > environment.yml

# Or export only the dependencies
conda list --export > requirements.txt

Importing the Environment

To import an existing environment file:

# Use the Conda environment file
conda env create -f environment.yml

# Or use the pip requirements file
pip install -r requirements.txt

📄 License

This project is licensed under the MIT License.

🤝 Contributing

Submitting Code

Before submitting code, please ensure that:

1. Run all test cases and ensure they pass:

pytest tests/

2. Ensure the code adheres to the project's coding standards.
3. Update relevant documentation and comments.
4. Create clear and descriptive commit messages.

Reporting Issues

Please create an issue in the GitHub repository and describe the problem in detail.