A developer demonstrates building a Model Context Protocol (MCP) server using the Haskell programming language, connecting it to Gemini CLI for AI tool integration. The project proves MCP's language-agnostic design, moving beyond Python to leverage Haskell's strengths in building reliable, purely functional systems for complex tasks.

The process involves setting up a local HTTP transport MCP server, validating the connection with Gemini CLI, and then containerizing the application for deployment. The key step is using Google Cloud Run to host the remote server, allowing the local Gemini CLI client to interact with a cloud-hosted Haskell MCP service.

This approach showcases how developers can use specialized languages like Haskell for AI tooling, not just Python. By deploying to Google Cloud Run, the workflow enables scalable, distributed MCP servers. The final validation confirms that the local Gemini CLI can successfully call tools on a remote Haskell server, bridging functional programming with modern AI ecosystems.