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Your API has a new consumer

Your OpenAPI was designed for humans. Is it ready for AI?

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.

OpenAPI definition used only for your audit No account to start Scored in 48 hours
PayQuicker85
Binance71
Your API?

Why your audience changed

For a decade, your API had one consumer.

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.

Reader
What it reads
Where it breaks
Developers human
Reference docs, examples, your SDK
Rarely blocked — fills gaps with judgment and a support ticket
Vibe coders prompt-first
An assistant grounded in your OpenAPI & SDK
The assistant lacks authoritative context and invents calls
Coding agents machine
The OpenAPI definition and generated SDK, directly
Ambiguous types, missing discriminants, undocumented errors
Chat apps & agents runtime
Your MCP server / tools, live at runtime
Tools are unreachable, bloated, or destructive without guardrails

What it costs you

The gap is already visible to your customers.

You won't see it as a bug report. AI failures against your API are quiet — which is exactly what makes them expensive.

// silent churn

Adoption you never see leave

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.

// support load

Tickets from code you didn't write

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.

// procurement

"Are you agent-ready?"

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

When an API is AI-ready, the numbers move.

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.

Faster integration, start to first working call
48%
Lower development cost on the same task
65%
Fewer tokens consumed by the agent
~0
Hallucinated endpoints and broken auth flows
Controlled study 6 commercial APIs · 3 models · 3 languages · Read the methodology →

What you get

Not a linter. A measured verdict with a fix plan.

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.

Foundation The specification itself

API Standards Compliance & Validation

Is your OpenAPI valid, structurally correct, and standards-conformant — clean enough to import and build on without breaking everything downstream?

Human Readiness For the developers who integrate your API

Documentation Readiness

Do your descriptions, examples, and tags generate clear, complete reference docs — or leave developers filling the gaps themselves?

SDK Readiness

Do your naming, types, and structure generate clean, idiomatic SDKs — or awkward client code no one wants to ship?

AI Readiness For the AI agents and assistants now reading it

AI Coding Assistant Readiness

Can Claude, Cursor, and Copilot model your API from the spec alone — schemas, types, formats, responses — and write integration code that actually works?

Agentic Tool-Use Readiness

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?

We score how readily an MCP surface can be generated and operated from your OpenAPI definition — not whether you've shipped one yet.

Industry benchmarks

We've scored the APIs your buyers compare you to.

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.

You Your API Not yet scored — see exactly where you'd land against these ? Get your score →

Real audits of public OpenAPI definitions; the linked reports are teasers from the full audit. Where would your API land? →


Why APIMatic

Other tools run checks. We hand you the fix.

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.

The audit engine · automation

Reproducible, defensible analysis

  • Thousands of deterministic rules — the same result every run.
  • AI-readiness rules a linter can't catch: clarity, disambiguation, examples, completeness.
  • Simulated agent scenarios run end-to-end, live over MCP — behaviour tested, not just structure.
  • Mandatory blocker caps, so a broken API can't score well by accident.
The human expert layer · judgment

Prioritisation tuned to your business

  • An engineer walks the integration as a developer, a vibe coder, and an agent would.
  • Competitive context against the APIs you're actually measured against.
  • A board-ready roadmap your team can act on immediately.
  • A 45-minute session with every audit — findings, not a sales pitch.

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

From OpenAPI to score in 48 hours.

Free for API providers during our launch. No pitch deck, no commitment to start.

01

Submit your OpenAPI definition

Share your OpenAPI definition or a public API URL. Takes two minutes — no account required to start.

02

We run the audit

Deterministic engine, AI-readiness analysis, and live agent runs over MCP. Score delivered within 48 hours.

03

Review with an expert

A 45-minute session to walk the findings, prioritise fixes, and map the highest-leverage work for your team.

Your OpenAPI, handled carefully

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."
— Nathaniel Olson, Sr. Product Manager, PayPal
PayPal

Get your audit

See your score.

Most teams are surprised. Either way, you'll know exactly where you stand — and what to do next.


Questions

Before you submit

What counts as a "qualified API provider"?
A company offering a public or partner-facing API with an OpenAPI definition (or a documented public endpoint we can read). During launch the audit is free for these providers; we'll let you know within a day if your API is a fit.
What do you do with my OpenAPI definition?
We use it only to run your audit. It is never used to train models, and we delete it on request once your report is delivered. If your OpenAPI is public, nothing leaves your control that wasn't already published.
How is the score calculated?
Each of the five pillars — across Foundation, Human Readiness, and AI Readiness — is scored 0–100 from thousands of deterministic checks plus AI-readiness analysis and live agent runs. They roll into one composite — but a single critical finding caps that composite, so a broken auth flow or a definition-breaking conflict can't be averaged away by clean pillars elsewhere. The full model ships in the appendix of your report.
Do I need an MCP server already?
No. Agentic Tool-Use Readiness scores how cleanly an MCP surface can be generated and operated from your OpenAPI definition — credential separation, tool schemas, auth routing, safe operations. You're measured on readiness, not on whether you've shipped one.
Which AI tools do you test against?
Coding assistants including Claude, Cursor, and Copilot for integration-code accuracy, and live tool execution over MCP for runtime agents. We run the same scenarios a real builder would.
Is 48 hours realistic — and what happens after launch?
Yes, for the OpenAPI-level audit; the deterministic engine is fast and the expert review is scheduled within that window. After the free launch period, deeper audits and the automated fixes — generated SDKs, MCP servers, Context Plugins, and docs — are paid. The audit itself stays the honest starting point.