Video intelligence is an unusual MCP workload. Operations are asynchronous and long-running (indexing a 30-minute video takes minutes, not milliseconds), inputs span local files and remote URLs, and the tool surface is wide; search, analysis, embeddings, and entity recognition each carry distinct parameter shapes and response patterns.
When we set out to build a production MCP server for TwelveLabs' video understanding platform, these constraints became a useful stress test for the protocol itself. This talk traces the builder's journey from first commit to published npm package (twelvelabs-mcp), covering the decisions that shaped a 19-tool MCP server and the patterns that emerged along the way.
Attendees will leave with concrete patterns for building MCP servers around API-first platforms (particularly those with async workflows, multimodal inputs, and wide tool surfaces), along with a clear picture of how MCP servers can evolve into full agent plugins.
