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Most MCP servers fail the same way: they expose observability data without understanding what AI models need to reason effectively. The result? Tools that overwhelm models with metrics, miss critical context, and introduce unnecessary security exposure.
At Multiplayer, we built an MCP server to give AI coding assistants access not just to production telemetry but to full stack data: frontend screens and data, backend traces, logs, and request/response content and headers. What we learned challenges the "more data is better" assumption that drives most integrations.
This talk shares the hard lessons from moving an MCP server into production. You'll learn why filtered, intent-driven context outperforms comprehensive data access, how to design tools that align with developer workflows rather than API surfaces, and the security trade-offs that matter when LLMs query your observability stack.
We'll cover practical design patterns for MCP servers in the observability space: scoping data by blast radius, surfacing relationships over raw metrics, and handling authentication without compromising developer experience. This talk is about what works when AI meets production systems.
