This product was not featured by Product Hunt yet. It will not be visible on their landing page and won't be ranked (cannot win product of the day regardless of upvotes).
Forecall
Lint your MCP tool descriptions before AI agents call them
AI agents choose and call MCP tools by reading their names, descriptions and input schemas. Vague text means the wrong tool, wrong arguments, or no call at all. Forecall scores each tool out of 100 on six items (purpose, when to use, arguments, return value, constraints, examples), finds tools that are easy to mix up, and explains every issue with a code and severity. Free on the web with no sign-up, or run npx forecall lint locally and in CI.
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?
Evaluations are live: real models (Claude Opus 5.5 and Sonnet 5.5, GPT-6 Astra and 6.1 Sol) answer utterances written for each of your tools, and you see tool choice, argument validity and false calls per model. On the Pro and Team plans.
The Failure KB is live: an MCP server of known MCP tool failures and their workarounds, in the official MCP Registry. Connect your agent with npx forecall setup.
The linter is now open source (Apache-2.0): https://github.com/forecall/forecall-cli. Two CI gates a reader asked for shipped the same day: --min-tool-score and --fail-on.
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 🙌
About Forecall on Product Hunt
“Lint your MCP tool descriptions before AI agents call them”
Forecall was submitted on Product Hunt and earned 6 upvotes and 3 comments, placing #38 on the daily leaderboard. AI agents choose and call MCP tools by reading their names, descriptions and input schemas. Vague text means the wrong tool, wrong arguments, or no call at all. Forecall scores each tool out of 100 on six items (purpose, when to use, arguments, return value, constraints, examples), finds tools that are easy to mix up, and explains every issue with a code and severity. Free on the web with no sign-up, or run npx forecall lint locally and in CI.
Forecall was featured in API (98.8k followers), Developer Tools (521.1k followers) and Artificial Intelligence (481k followers) on Product Hunt. Together, these topics include over 229.2k products, making this a competitive space to launch in.
Who hunted Forecall?
Forecall was hunted by Hiroki Nomura. A “hunter” on Product Hunt is the community member who submits a product to the platform — uploading the images, the link, and tagging the makers behind it. Hunters typically write the first comment explaining why a product is worth attention, and their followers are notified the moment they post. Around 79% of featured launches on Product Hunt are self-hunted by their makers, but a well-known hunter still acts as a signal of quality to the rest of the community. See the full all-time top hunters leaderboard to discover who is shaping the Product Hunt ecosystem.
Want to see how Forecall stacked up against nearby launches in real time? Check out the live launch dashboard for upvote speed charts, proximity comparisons, and more analytics.
Hiroki Nomura