Every MCP server pays a hidden tax: the moment an AI agent connects, your entire tools/list gets injected into its context window. Every tool name, every description, every JSON schema — before the user has typed a single word.
Full disclosure up front: every mistake in this story is our own making — nobody handed us 76 tools, we grew them.
Doriku is a shared control plane for AI coding agents — Claude Code, Codex, Cursor, Gemini CLI, Windsurf, or any MCP-compatible tool talks to one task/memory/decision store. We grew fast and organically: task CRUD, dependency tracking, file locks, decisions, semantic memory, workflows, cost caps, approvals… Each feature politely added its own tools. One day we counted: 76 tools, 38,818 bytes of tools/list payload, loaded into every session of every agent, every day — before any work happened.
The problem isn't the number, it's the multiplication
38 KB doesn't sound like much until you multiply it: N agents × M sessions per day × every reconnect. For a heavy user we measured 6,298 API calls over 30 days across multiple daily sessions. That's megabytes of context per user per month spent on tool definitions — pure overhead that competes with actual working context and dilutes the model's attention across 76 choices.
