Building an MCP server for financial data: lessons learned

Sidney Bissoli shares technical insights from developing 'bcb-br-mcp', an open-source Model Context Protocol (MCP) server that provides AI agents with data from the Brazilian Central Bank. The project highlights the complexities of building reliable integrations for AI, moving beyond simple API wrappers. Key challenges included handling misleading HTTP 200 responses from upstream APIs, managing inconsistent data formatting, and ensuring parity across different transport layers like stdio and Cloudflare Workers. Bissoli emphasizes the importance of robust testing, particularly for data provenance and output schemas, to prevent AI agents from hallucinating or misinterpreting financial metrics. The article serves as a practical guide for developers building MCP servers, advocating for a single-registry architecture, rigorous characterization tests, and careful handling of runtime-specific constraints. By documenting these 'sharp edges,' the author provides a roadmap for creating more resilient and trustworthy AI-accessible data services.