# 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](https://date.nager.at)
- **API Documentation**: [https://weather-mcp.example.com/docs](https://weather-mcp.example.com/docs)
## Available Tools
This server provides the following tools:
- **`example_tool`**: Placeholder tool (to be implemented)
*Note: Replace `example_tool` with actual tes-mcp-server API tools based on the documentation.*
## Installation
### Using Docker (Recommended)
1. Clone this repository:
```bash
git clone https://github.com/Traia-IO/tes-mcp-server-mcp-server.git
cd tes-mcp-server-mcp-server
```
2. Run with Docker:
```bash
./run_local_docker.sh
```
### Using Docker Compose
1. Create a `.env` file with your configuration:
```env
PORT=8000
```
2. Start the server:
```bash
docker-compose up
```
### Manual Installation
1. Install dependencies using `uv`:
```bash
uv pip install -e .
```
2. Run the server:
```bash
uv run python -m server
```
## Usage
### Health Check
Test if the server is running:
```bash
python mcp_health_check.py
```
### Using with CrewAI
```python
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:
```json
{
"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](LICENSE)
