MCP servers today are blind to the client's context budget. In production, we watched a single list call return 161k tokens. Tool schemas alone consumed 37% of a 200k context window. One customer's sprint data retrieval filled 80k+ tokens and collapsed the agent's reasoning entirely. Organizations with heavy custom fields caused tool schemas to balloon unpredictably. These aren't edge cases - they're the default experience at enterprise scale. The MCP protocol currently has no mechanism for servers to understand or respect context constraints. Every response is a best-effort data dump. This talk presents real production telemetry from building and operating an enterprise MCP server, then proposes 3 strategies to solve the context constraints at enterprise scale which community should consider: progressive schema disclosure, server-side response projections, SQL based tools for multi-object queries. We'll share an open reference implementation of progressive discovery that reduced effective schema size by 70%. The goal: move this conversation from "each server figures it out alone" to "the ecosystem has shared conventions."
