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Exposes public weather and climate data through a standardized API, allowing AI agents to retrieve current conditions, 7-day forecasts, and historical data. It
Exposes public weather and climate data through a standardized API, allowing AI agents to retrieve current conditions, 7-day forecasts, and historical data. It enables weather-aware automation and data enrichment for conversational agents and travel planning.
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.
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.
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
.env file with your configuration:
PORT=8000
2. Start the server:
```bash
docker-compose up
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
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)
python mcp_health_check.pyTo add new tools, edit server.py and:
@mcp.tool() decorated functionsdeployment_params.json with the tool names in the capabilities arrayThe 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!
This server is designed to be deployed on Google Cloud Run. The deployment will:
/mcp endpoint for client connectionsPORT: Server port (default: 8000)STAGE: Environment stage (default: MAINNET, options: MAINNET, TESTNET)LOG_LEVEL: Logging level (default: INFO)docker logs <container-id>Добавь это в claude_desktop_config.json и перезапусти Claude Desktop.
{
"mcpServers": {
"tes-mcp-server": {
"command": "npx",
"args": []
}
}
}