Your API has a new consumer
AI agents don't ask when your OpenAPI is ambiguous — they guess. The guess compiles, ships, and fails silently. You don't get a support ticket. You get a silent churn.
Why your audience changed
A developer, with your documentation open in another tab. That assumption is now wrong. Three new classes of consumer read your API — and most of the integration traffic is shifting to them. They each read it differently, and none of them route around a gap the way a person does.
A developer routes around an ambiguous API. A machine integrates it — and ships it.
What it costs you
You won't see it as a bug report. AI failures against your API are quiet — which is exactly what makes them expensive.
When an agent misuses your API, the developer doesn't file a ticket. They get frustrated, abandon the integration, or ship code that fails later. You see the churn, never the cause.
A growing share of integration support traces back to AI-generated code that hallucinated your API. Your team debugs problems a clearer OpenAPI would have prevented.
Enterprise buyers are starting to ask before they sign. An unscored API reads as risk. A score with a fix plan reads as maturity — and shortens the cycle.
The payoff
In controlled runs inside Claude and Cursor, the same integration task against an AI-ready OpenAPI finished faster, cost less, and stopped fabricating calls. The audit tells you how far you are from that — and what it takes to close the gap.
What you get
Five pillars in three groups — Foundation, Human Readiness, and AI Readiness — each scored 0–100 and rolled into one composite score leadership can act on, backed by evidence your engineers can execute line by line.
Is your OpenAPI valid, structurally correct, and standards-conformant — clean enough to import and build on without breaking everything downstream?
Do your descriptions, examples, and tags generate clear, complete reference docs — or leave developers filling the gaps themselves?
Do your naming, types, and structure generate clean, idiomatic SDKs — or awkward client code no one wants to ship?
Can Claude, Cursor, and Copilot model your API from the spec alone — schemas, types, formats, responses — and write integration code that actually works?
Exposed as callable tools over MCP, can an agent find, call, and safely operate your endpoints end-to-end — clean tool schemas, correct auth routing, no destructive surprises?
Industry benchmarks
Same engine, same rules — run against the public OpenAPI definitions in your market. Click any score to open its sample audit report, then find the one line that's still blank.
Real audits of public OpenAPI definitions; the linked reports are teasers from the full audit. Where would your API land? →
Why APIMatic
Twelve years of generating SDKs, docs, and tools from real-world OpenAPI definitions sit behind this audit — three independent methods, layered so each one covers the others' blind spots.
A linter checks structure. We watch real agents try to use your API. For the gaps we can automate, we fix them for you — generated SDKs, MCP servers, AI Context Plugins, and docs, ready the moment you are. Learn about Context Plugins →
How it works
Free for API providers during our launch. No pitch deck, no commitment to start.
Share your OpenAPI definition or a public API URL. Takes two minutes — no account required to start.
Deterministic engine, AI-readiness analysis, and live agent runs over MCP. Score delivered within 48 hours.
A 45-minute session to walk the findings, prioritise fixes, and map the highest-leverage work for your team.
We use your OpenAPI only to run your audit. It's never used to train models, and we delete it on request after we deliver your report.
"APIMatic helped us prepare our platform for the future of Agentic AI."
Get your audit
Most teams are surprised. Either way, you'll know exactly where you stand — and what to do next.
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