Forecall - Lint your MCP tool descriptions before AI agents call them

Hi Product Hunt! AI agents pick a tool by reading its name, description and input schema, and nothing else. When we scored 8 well-known MCP servers (126 tools), their averages ranged from 33 to 70 out of 100, and Notion's official server alone had 57 pairs of tools an AI can easily mix up. Forecall is a static linter for that text. It scores each tool on six items, explains every issue with a code and a severity, and points out tools that are easy to confuse. It's free on forecall.dev with no sign-up, and the same rules run locally with npx forecall lint (nothing is uploaded), so you can fail a CI job when the average drops. Next, we're building Evaluations that measure how often real models choose and call your tools correctly. I'd love your feedback on the scoring rules: what should a good tool description say?

A bit more on what the scores look like in practice 👇 It isn't about how many tools you have. The Playwright MCP server has 25 tools and zero confusable pairs (average 41.9), while Notion's official server has 24 tools and 57 pairs (average 33.0). Notion's lowest-scoring tools are API-query-data-source (20/100), API-create-a-comment (24) and API-update-a-data-source (24). Three edits worth trying first, each one of the six items: 1. A "when to use" line that names the neighbor: "Use this to …; to … instead, use X." 2. One sentence on what it returns. 3. One example call with real argument values. To try it, paste the JSON your server returns for tools/list at forecall.dev. No sign-up needed. Post your average below and I'll reply with the first change I'd make 🙌