GoKawiilI lint-scanned 36 popular MCP servers. A third of them are failing your agent.
Your MCP server can be 100% spec-compliant and still be unusable by an agent.
The Model Context Protocol spec tells you how to transport tools: JSON-RPC framing, capability negotiation, schema shapes. It says nothing about whether a model can actually use what you serve — whether it picks the right tool out of your catalog, fills the arguments correctly, or burns 8k tokens parsing your schemas on every single request.
I integrate first- and third-party MCP connectors into a production AI agent for a living, and I kept seeing the same failure: servers that pass every compliance check, yet the model calls the wrong tool, hallucinates arguments, or ignores the tool entirely. The problems were never in the protocol layer. They were in the parts no one lints: descriptions, naming, schema design.
So I wrote mcpgrade — a Lighthouse-style scorecard for MCP servers. One command, no API key, report in seconds:
npx mcpgrade --stdio "npx -y your-mcp-server"
Then I pointed it at 36 popular servers. It did not go great.
The results
Full sortable table: [https://tengli.dev/mcp-leaderboard.html]. The short version (static analysis, point-in-time snapshot; servers marked (archived) are unmaintained reference implementations, included because they're still widely installed and copied):
Top of the class (A): brave-search (archived), exa, google-maps (archived), slack (archived), perplexity-ask, @shopify/dev-mcp, @apify/actors-mcp-server, airbnb, figma-developer-mcp, tavily, gitlab (archived), elastic, shrimp-task-manager, and more — 15 of 36.
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