Creating a Custom MCP Client with Elixir for AI Tool-Calling

This article explores the journey of building a client for the MCP (Multi-Channel Processing) framework in Elixir, following a series on AI systems implementation. The author explains the limitations of the retrieval-augmented generation (RAG) approach and how transitioning to agentic tool-calling has led to more effective AI interactions by allowing the language model (LLM) to autonomously determine the best information retrieval strategy. The piece details lessons learned while developing a custom MCP client that tightly integrates with their existing permission systems to ensure security and effectiveness. The advantages of using Elixir for such implementations are highlighted, showcasing how its features like lightweight processes and concurrency make managing complex workflows easier. User control, transparency, and source attribution are emphasized as critical design principles, making the MCP client robust and user-friendly. The article concludes with reflections on the overall design process and offers insights for others embarking on similar AI tool creation journeys.