ClimateTrace MCP Server

The ClimateTraceKMP MCP server turns the rich, publicly‑available emissions data from Climate Trace into a first‑class AI tool. Instead of having an assistant crawl a website or parse CSVs, the server exposes a concise API that returns per‑country sector emissions in JSON. This removes the need for custom web scrapers, reduces latency, and guarantees that every query hits a single, well‑documented endpoint. For developers building data‑driven assistants, the result is a reliable, low‑overhead source of climate information that can be queried on demand.

At its core, the server is a Kotlin Multiplatform project. It leverages the Kotlin MCP SDK to publish an endpoint that returns structured emission data. The same shared code powers a wide array of front‑end targets—iOS, Android, desktop, web (Wasm and Kotlin/JS), SwiftUI, Compose Multiplatform, and even a Kotlin Notebook—so the data model remains consistent across all consumer applications. This tight coupling means that a change to the data source or schema propagates automatically to every client, ensuring parity and reducing maintenance overhead.

Key capabilities include:

• Sector‑by‑sector breakdowns per country, enabling fine‑grained analysis of CO₂ footprints.
• Multiplatform access: the server can be invoked from any language that speaks HTTP, but it also integrates natively with Claude Desktop via the MCP configuration file.
• Extensibility: developers can add new resources or tools (e.g., time‑series queries, custom filters) without touching the client code.
• Zero‑dependency deployment: a single shaded JAR () runs on any JVM, making it trivial to host in Docker or a serverless environment.

Typical use cases include:

• Climate‑aware chatbots that answer “What is the CO₂ emission of Germany in the energy sector?” or “Show me a trend for India’s transport emissions.”
• Data‑visualization assistants that generate charts or tables on demand, feeding the UI with fresh values from the MCP endpoint.
• Educational tools where students can query historical emissions and compare policy impacts without leaving the chat interface.

Integrating the server into an AI workflow is straightforward: after building the shaded JAR, add a configuration block to Claude Desktop’s developer settings that points to the executable. Once registered, any assistant can invoke the tool by name and receive a structured payload that can be consumed by downstream logic or displayed directly to users. This plug‑and‑play model saves developers from reinventing data ingestion pipelines and lets them focus on the conversational logic that delivers real value.