MeasureSpace MCP Server

Overview

The MeasureSpace MCP server bridges the gap between AI assistants and real‑time environmental data. By exposing weather, climate, air quality forecasts, geocoding, and astronomy information through the Model Context Protocol, it allows developers to enrich conversational agents with precise, location‑specific insights. This eliminates the need for manual API integration and lets assistants answer questions about tomorrow’s temperature, long‑term climate trends, or the nearest city to a set of coordinates—all with minimal effort.

The server is built on FastAPI and the MCP framework, ensuring low latency and robust request handling. It aggregates multiple MeasureSpace API endpoints into a single MCP surface, providing tools such as:

• Hourly and daily weather forecasts for up to five days and fifteen days, respectively
• Nine‑month climate projections that can inform long‑term planning or agricultural decisions
• Air quality indices (hourly and daily) covering a four‑day horizon
• Geocoding utilities to translate city names into coordinates or find the nearest town for a latitude/longitude pair
• Timezone and astronomy data (sunrise, sunset) that support contextual scheduling or travel planning

These capabilities are packaged as distinct MCP tools, each with its own prompt template and sampling strategy. Developers can invoke them directly from an AI assistant’s dialogue, allowing the agent to fetch fresh data on demand or embed it within a broader narrative.

In practical scenarios, MeasureSpace MCP shines in:

• Travel and logistics: A travel assistant can recommend itineraries based on upcoming weather, daylight hours, and local air quality.
• Agricultural advisory: Farmers receive climate forecasts to schedule planting or irrigation.
• Health and wellness apps: Users get personalized air quality alerts tied to their location.
• Smart city dashboards: City planners query real‑time environmental metrics for decision support.

By integrating with existing MCP workflows, the server offers a plug‑and‑play experience: once registered, any compliant AI client can call its tools without additional code. Its unique advantage lies in the breadth of environmental data consolidated into a single, well‑documented MCP interface—saving developers time and ensuring consistent, up‑to‑date information for their users.