Valencia Smart City MCP Server

The Valencia Smart City MCP Server addresses the growing need for real‑time urban data integration in AI applications. Urban planners, commuters, and developers often require up‑to‑date traffic flows, bike‑sharing availability, air quality metrics, and weather forecasts to make informed decisions. By exposing these datasets through a Model Context Protocol interface, the server allows language models such as Claude to query and analyze live city information without leaving the conversational context.

At its core, the server aggregates data from multiple authoritative sources: traffic conditions are sourced directly from Valencia’s transportation network; bike‑sharing statistics come from the city’s Valenbisi system; air quality readings are pulled from municipal monitoring stations; and weather data is provided by the Open‑Meteo API. Each dataset is refreshed continuously, ensuring that assistant responses reflect current conditions rather than stale historical data.

Key capabilities include:

• Traffic Tools that return segment‑level status, congestion summaries, and allow keyword searches for specific roads.
• Bike‑Sharing Tools that locate stations with available bikes, filter by minimum capacity, and retrieve detailed station status.
• Air‑Quality Tools that offer citywide summaries, pollutant‑specific measurements, nearest station lookups, and geospatial mapping data.
• Weather Tools that deliver current conditions, multi‑day forecasts, and historical weather trends.

These features enable a range of practical use cases. A navigation assistant can suggest alternative routes when congestion spikes, while a health‑focused chatbot could warn users about high pollution near their location. Urban analytics platforms can embed the server to surface real‑time dashboards, and smart‑home systems might trigger ventilation controls based on incoming air quality data. Because the MCP interface is standardized, any LLM client that supports MCP can tap into these resources without custom API wrappers.

Integrating the server into AI workflows is straightforward: developers define tool calls in their prompt templates, and the model orchestrates queries to the appropriate MCP endpoint. The result is a seamless blend of conversational intelligence and live city data, empowering developers to build contextually aware applications that respond dynamically to the evolving urban environment.