Lighthouse for MCP servers — score any MCP server on agent usability, not just spec compliance
Lighthouse for MCP servers. Your server can be 100% spec-compliant and still fail agents — vague descriptions, token-bloated schemas, confusable tool names. mcpgrade scores what compliance checkers can't: whether an LLM can actually use your tools.
Zero config. No API key. Report in seconds.
Category Weight Examples --------- Descriptions 30% missing/too-short descriptions, undocumented params, placeholder text, duplicate descriptions Schema design 30% missing types, no required array, additionalProperties: true, prose-instead-of-enum, deep nesting Naming 15% confusable names (getuser vs getusers), generic verbs (process), mixed conventions Token cost 15% catalog total budget, per-tool budget — agents pay your schema on every request Consistency 10% catalog-wide uniformity; with --probe, live checks that error messages help the model self-correct
Every finding comes with a concrete fix. Scores are density-normalized: 3 broken tools out of 3 is an F; 3 out of 30 is a dent.
I integrate first-party and third-party MCP connectors into a production AI agent for a living. Most MCP servers fail agents in the same ten ways — none of which show up in a spec compliance check. So I wrote the linter I wished server authors had run before shipping.
Different tools, different questions. mcp-lint checks whether your tool schemas parse correctly across clients (Claude, Cursor, OpenAI strict mode, ...) — syntax-level compatibility. mcpgrade measures whether a model can actually use your tools — description quality, naming confusion, token economics, and live LLM tool-selection accuracy
From the project README.
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