USDC Server

This is an MCP (Model Context Protocol) server that provides access to the USDC MCP Server API. It enables AI agents and LLMs to interact with USDC MCP Server through standardized tools.

Features

• 🔧 MCP Protocol: Built on the Model Context Protocol for seamless AI integration
• 🌐 Full API Access: Provides tools for interacting with USDC MCP Server endpoints
• 🐳 Docker Support: Easy deployment with Docker and Docker Compose
• ⚡ Async Operations: Built with FastMCP for efficient async handling

API Documentation

• USDC MCP Server Website: https://petstore3.swagger.io/api/v3
• API Documentation:

Available Tools

This server provides the following tools:

• example_tool: Placeholder tool (to be implemented)

Note: Replace example_tool with actual USDC MCP Server API tools based on the documentation.

Installation

Using Docker (Recommended)

1. Clone this repository:

   git clone https://github.com/Traia-IO/usdc-mcp-server-mcp-server.git
   cd usdc-mcp-server-mcp-server
   

2. Run with Docker:

   ./run_local_docker.sh
   

Using Docker Compose

1. Create a .env file with your configuration:

PORT=8000

2. Start the server:
```bash
docker-compose up

Manual Installation

1. Install dependencies using uv:

   uv pip install -e .
   

2. Run the server:

uv run python -m server

## Usage

### Health Check

Test if the server is running:
```bash
python mcp_health_check.py

Using with CrewAI

from traia_iatp.mcp.traia_mcp_adapter import create_mcp_adapter

# Connect to the MCP server
with create_mcp_adapter(
    url="http://localhost:8000/mcp/"
) as tools:
    # Use the tools
    for tool in tools:
        print(f"Available tool: {tool.name}")
        
    # Example usage
    result = await tool.example_tool(query="test")
    print(result)

Development

Testing the Server

1. Start the server locally
2. Run the health check: python mcp_health_check.py
3. Test individual tools using the CrewAI adapter

Adding New Tools

To add new tools, edit server.py and:

1. Create API client functions for USDC MCP Server endpoints
2. Add @mcp.tool() decorated functions
3. Update this README with the new tools
4. Update deployment_params.json with the tool names in the capabilities array

Deployment

Deployment Configuration

The deployment_params.json file contains the deployment configuration for this MCP server:

{
  "github_url": "https://github.com/Traia-IO/usdc-mcp-server-mcp-server",
  "mcp_server": {
    "name": "usdc-mcp-server-mcp",
    "description": "Usdc mcp server",
    "server_type": "streamable-http",
"capabilities": [
      // List all implemented tool names here
      "example_tool"
    ]
  },
  "deployment_method": "cloud_run",
  "gcp_project_id": "traia-mcp-servers",
  "gcp_region": "us-central1",
  "tags": ["usdc mcp server", "api"],
  "ref": "main"
}

Important: Always update the capabilities array when you add or remove tools!

Google Cloud Run

This server is designed to be deployed on Google Cloud Run. The deployment will:

1. Build a container from the Dockerfile
2. Deploy to Cloud Run with the specified configuration
3. Expose the /mcp endpoint for client connections

Environment Variables

• PORT: Server port (default: 8000)
• STAGE: Environment stage (default: MAINNET, options: MAINNET, TESTNET)
• LOG_LEVEL: Logging level (default: INFO)

Troubleshooting

1. Server not starting: Check Docker logs with docker logs <container-id>
2. Connection errors: Ensure the server is running on the expected port3. Tool errors: Check the server logs for detailed error messages

Contributing

1. Fork the repository
2. Create a feature branch
3. Implement new tools or improvements
4. Update the README and deployment_params.json
5. Submit a pull request

License

MIT License