Abdul Hannan ShaikhAbdul Hannan ShaikhTechnical Product Manager

Can an AI agent actually use your API?

One week. A scored read of your surface, docs and error handling, and the gaps ranked by what they cost you at integration time.

Write to meOr run the diagnostic yourself

One week, $1,200, fixed. Scope agreed before anything starts.

The rubric is public. Run it yourself.

The score comes from A[P]Iaudit, a free and open-source diagnostic that reads an OpenAPI spec and names the gaps. No signup, and the source is on GitHub.

So you can argue with the method before you pay for an opinion, which is the opposite of how this category usually works.

And the measurement is published.

Four controlled experiments on two open-source .NET codebases, an agent working with a context layer against one working from web search alone. Compile and runtime errors fell from 16 to 1, tokens 65%, cost 48%, and integration ran about twice as fast.

APIMatic published the whole method, so the numbers are checkable without taking my word. The APIs under test there are open source, not customers.

A report written for the engineers who’ll fix it.

Three things, in the order they turn out to be useful.

API agent readiness read

One week, $1,200 fixed

Turnaround inside a week.

  • A score against the public rubric, so the method is arguable.
  • Every gap named, in your surface, docs and error handling.
  • The gaps ranked by what each one costs at integration time.

For API products and developer platforms with agents already integrating.

Write to meOr book a 30-minute call

Two sentences about the API is plenty. Thirty minutes if you’d rather talk, no charge either way.

Questions people actually ask

What makes an API agent-ready?

An agent reads the spec, the error bodies and the examples. It can’t ask a colleague what a field means, and it can’t guess past a 400 that says “invalid request”. Agent-ready means the surface answers those questions on its own: complete schemas, errors that name the field and the fix, examples that run, and auth an agent can complete without a human in the loop.

How is this different from a normal API review?

A normal review asks whether the API is well designed. This asks whether a machine can integrate it without a human reading the docs, which turns out to be a different question. Plenty of well-designed APIs fail it, usually on error bodies and on examples that assume context the agent doesn’t have.

Do we need an MCP server?

Usually not, and that's often the first thing the read tells you. An MCP server in front of an API that agents already stall on just moves the problem. Where a server is the right answer, the read says what it needs to wrap.

What do we get at the end?

A written report: a score against the public rubric, every gap named, and the gaps ranked by what they cost at integration time. It's written for the engineers who'll fix it.

How much does it cost and how long does it take?

One week, $1,200, fixed. Scope is agreed before anything starts, and the diagnostic the score is built on is free and open source, so you can run it yourself first.