iflow-mcp_simovilab-transit 0.1.0

Model Context Protocol server enabling AI assistants to access transit information

Infobús MCP (servidor)

Interfaz de inteligencia artificial en sistemas de información para personas usuarias del transporte público

Fabián Abarca Calderón

Nota: manejador de paquetes de Python uv.

uv init transit
cd transit

uv venv
source .venv/bin/activate

uv add "mcp[cli]" httpx

touch transit.py

@mcp.tool()
async def get_forecast(latitude: float, longitude: float) -> str:
    """Get weather forecast for a location.

    Args:
        latitude: Latitude of the location
        longitude: Longitude of the location
    """
    # First get the forecast grid endpoint
    points_url = f"{INFOBUS_API_BASE}/points/{latitude},{longitude}"
    points_data = await make_infobus_request(points_url)

    if not points_data:
        return "Unable to fetch forecast data for this location."

    # Get the forecast URL from the points response
    forecast_url = points_data["properties"]["forecast"]
    forecast_data = await make_infobus_request(forecast_url)

    if not forecast_data:
        return "Unable to fetch detailed forecast."

    # Format the periods into a readable forecast
    periods = forecast_data["properties"]["periods"]
    forecasts = []
    for period in periods[:5]:  # Only show next 5 periods
        forecast = f"""
{period['name']}:
Temperature: {period['temperature']}°{period['temperatureUnit']}
Wind: {period['windSpeed']} {period['windDirection']}
Forecast: {period['detailedForecast']}
"""
        forecasts.append(forecast)

    return "\n---\n".join(forecasts)