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AgenticScore
Score your OpenAPI spec for AI agent readiness
Score your OpenAPI spec for AI agent readiness across six dimensions. Find what LLMs struggle with before your users do.
Stripe's OpenAPI spec has zero examples across 587 operations. None of its operations document error responses. Yet Stripe's docs are the reference founders cite for what good developer documentation looks like.
The disconnect matters because agent frameworks (LangChain, OpenAI function calling, MCP) load the OpenAPI spec at runtime to build the agent's toolset. The spec is what the model sees. Not the docs site.
I built AgenticScore to measure how much context each spec actually gives the agent. Six dimensions: examples, semantic clarity, error handling, intent signals, parameter documentation, pagination. Each rule weighted by how unrecoverable its absence is when an agent is constructing a call.
I ran it against seven well-known APIs. Stripe scored 37 (F). OpenAI scored 32 (F). The highest score in the set was Plaid at 63 (C). No A's, no B's.
The CLI is free:
npx agenticscore score ./openapi.yaml
Full methodology, including every weight and the reasoning: https://agenticscore.dev/methodo...
Per-API teardowns: https://agenticscore.dev/leaderb...
Run it on your own spec and let me know what surprises you.
About AgenticScore on Product Hunt
“Score your OpenAPI spec for AI agent readiness”
AgenticScore was submitted on Product Hunt and earned 0 upvotes and 1 comments, placing #150 on the daily leaderboard. Score your OpenAPI spec for AI agent readiness across six dimensions. Find what LLMs struggle with before your users do.
On the analytics side, AgenticScore competes within API, Software Engineering and Developer Tools — topics that collectively have 654.8k followers on Product Hunt. The dashboard above tracks how AgenticScore performed against the three products that launched closest to it on the same day.
Who hunted AgenticScore?
AgenticScore was hunted by Thomas Slater. 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.
For a complete overview of AgenticScore including community comment highlights and product details, visit the product overview.
Thomas Slater