Running efficient MCP servers in production

Most MCP servers are built for demos, not production. They work great with hand-crafted examples but fall apart when real users task AI agents with complex, domain-specific workflows.

Teams quickly wrap their existing APIs in MCP servers and expect flawless performance. This is backwards. Your MCP server design determines whether AI agents succeed or fail at completing the tasks that matter to your business, period. In this talk, I'll reveal the specific patterns that separate production-ready MCP servers from development toys. You'll learn the counter-intuitive principles about tool design, response optimization, and error handling that most teams get wrong, and the practical strategies that actually work at scale.

Walk away with a battle-tested playbook for building MCP servers that don't just work in demos, but thrive under real agent pressure and deliver consistent results your users can depend on.