tes-mcp-server MCP Server

This is an MCP (Model Context Protocol) server that provides access to the tes-mcp-server API. It enables AI agents and LLMs to interact with tes-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 tes-mcp-server endpoints

• 🐳 Docker Support: Easy deployment with Docker and Docker Compose

• ⚡ Async Operations: Built with FastMCP for efficient async handling

API Documentation

• tes-mcp-server Website: https://date.nager.at

• API Documentation: https://weather-mcp.example.com/docs

Available Tools

This server provides the following tools:

• example_tool: Placeholder tool (to be implemented)

Note: Replace

Installation

Using Docker (Recommended)

1. Clone this repository:

   git clone https://github.com/Traia-IO/tes-mcp-server-mcp-server.git
   cd tes-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 tes-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/tes-mcp-server-mcp-server",
  "mcp_server": {
    "name": "tes-mcp-server-mcp",
    "description": "This mcp server exposes public weather and climate data through a standardized mcp-compatible api.
it allows ai agents to retrieve current weather conditions, 7-day forecasts, and historical climate data
for supported locations worldwide.

use cases include:
- ai-powered travel planning
- weather-aware automation
- data enrichment for conversational agents
",
    "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": ["tes-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