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The post provides production evidence that the widely cited ~15-tool MCP limit is a proxy for ambiguity rather than a hard count ceiling, and demonstrates that naming grammar, description-level routing instructions, and selection-focused evals can keep a 27-tool server accurate.
Design your MCP tools around what an agent needs to accomplish in one step — not what your REST API exposes — to reduce latency, token spend, and model reasoning errors in production.
Developers building MCP servers should design around a small number of parameterized verbs rather than mirroring their REST API surface, as tool count directly degrades model reliability and inflates token costs.
Teams building large MCP servers can adopt this domain-plus-permission file structure and seven-verb naming convention to keep tool sets predictable for both developers and AI models as the tool count scales.
Developers building or choosing AI-integrated tooling should take note: exposing personal knowledge bases via MCP is emerging as a practical pattern for persistent AI context — Hjarni is an early, opinionated example of what "MCP-first" product design looks like in practice.