How do I use tes-mcp-server?

1. Click on "Install Server". 2. Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state. 3. In the chat, type @ followed by the MCP server name and your instructions, e.g., "@tes-mcp-server get the 7-day weather forecast for London" That's it! The server will respond to your query, and you can continue using it as needed. Here is a step-by-step guide with screenshots.

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)

Clone this repository:
git clone https://github.com/Traia-IO/tes-mcp-server-mcp-server.git cd tes-mcp-server-mcp-server
Run with Docker:
./run_local_docker.sh

Using Docker Compose

Create a .env file with your configuration:

PORT=8000

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

Manual Installation

Install dependencies using uv:
uv pip install -e .
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

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

Adding New Tools

To add new tools, edit server.py and:

Create API client functions for tes-mcp-server endpoints
Add @mcp.tool() decorated functions
Update this README with the new tools
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:

Build a container from the Dockerfile
Deploy to Cloud Run with the specified configuration
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

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