MCP Server GPF

Overview

The Llm Experimentations MCP server is a sandbox designed to push the boundaries of what language‑model assistants can achieve when they interact with real-world geospatial services. Rather than being a single monolithic tool, it is a curated collection of experiments that demonstrate how an AI assistant can query the French National Institute for Geographic and Forest Information (IGN) APIs, perform geocoding, retrieve postal codes, and even generate client‑side code on the fly. By exposing these capabilities through MCP, developers can prototype conversational workflows that feel natural to end‑users while still leveraging powerful back‑end services.

At its core, the server solves the problem of bridging a conversational AI’s knowledge base with live data sources. In many production scenarios, static prompts are insufficient because the assistant must answer questions about up‑to‑date geographic information or produce code that calls a specific API. The MCP server provides a lightweight, modular way to expose these external services as actions that the AI can invoke on demand. This eliminates the need for manual API calls or complex middleware, allowing developers to focus on crafting intuitive dialogue flows.

Key features include:

• Geospatial search and geocoding: The server hosts examples that let ChatGPT look up commune information, resolve postal codes to INSEE identifiers, and test the limits of default geocoding tools.
• API integration demonstrations: Several experiments show how the assistant can call the APICARTO endpoints for postal codes and viticultural AOC maps, or tap into the Géoplateforme altitude service.
• Code generation: By providing an MCP action that returns client‑side code snippets, the server lets developers ask the AI to produce functional JavaScript or Leaflet scripts that directly consume IGN services.
• Modular MCP server implementations: The example illustrates how to build a custom MCP endpoint that forwards requests to the Géoplateforme, highlighting best practices for authentication and response shaping.

Real‑world use cases abound. A municipal website could integrate the server to let residents ask for their commune’s INSEE code or nearby viticultural zones, with the assistant returning a ready‑to‑use map widget. A GIS developer could prototype new data pipelines by asking the assistant to generate API calls, then copy the resulting code into their project. In education, instructors can demonstrate how conversational AI can serve as an interactive teaching assistant for geospatial concepts.

Integration into existing workflows is straightforward: the MCP server exposes a JSON‑over‑HTTP interface that any Claude or GPT‑based client can consume. Developers simply add the server’s URL to their AI’s action list, then craft prompts that reference the available actions. Because each experiment is self‑contained and documented in its own README, teams can cherry‑pick the components that match their needs without pulling in unnecessary complexity.

What sets this repository apart is its focus on experimentation rather than production readiness. All code snippets are generated by ChatGPT itself, and the authors explicitly note that creating GPTs with actions requires a paid license. This transparency encourages developers to treat the experiments as learning tools, iterate on them, and eventually tailor the patterns to their own MCP deployments.