An agent calls createProject, gets a valid response, and announces that the project is ready. Then the next request cannot find it. The tool call succeeded. The workflow did not.

That gap is worth designing for before you expose an API to an agent. Here is a small exercise: take one create-and-read flow and make the evidence for completion explicit.

Start with an observable outcome

Use a disposable project in a development environment. Define the outcome as: a project created under the current account can be retrieved by the ID returned from creation, with the expected name and owner. That sentence gives the agent a goal and gives the test runner something concrete to check.

Now split the work into four steps: create the project, capture its returned ID, retrieve that ID, and compare the result. Clean up the disposable project afterward. A cleanup failure should remain visible rather than erase the original failure.

createProject({ name: "workflow-fixture" })
  -> capture response.id
getProject({ id: response.id })
  -> assert returned id matches
  -> assert name == "workflow-fixture"
  -> assert owner matches the test account

This is illustrative pseudocode, not a Powerduck configuration format. The key is that the second call uses evidence from the first. A plausible ID invented by the model is not a substitute.

Give each layer a job

Your OpenAPI document describes operation inputs and possible responses. Keep required fields, response types, and operation identifiers accurate. The official specification defines these structures; it does not make your business outcome true merely because a response validates.

The MCP tool interface gives an agent a way to invoke those operations. The scenario supplies ordering, captured values, and outcome assertions. The reference documentation explains the behavior a person needs to understand. They should agree, but they do different work.

For an asynchronous create operation, replace an immediate read assertion with the documented status-check flow and a bounded wait. Choose the completion condition from your API behavior. A fixed sleep that happens to pass today is weak evidence.

Keep the failure trace useful

When the read fails, retain the operation, sanitized arguments, response status, returned ID, and failed assertion. Distinguish a missing resource from an authorization failure. Check whether the create and read calls used the same environment and identity before changing the schema.

Then ask AI for a bounded investigation: explain why this create-and-read scenario failed using these two responses and this contract. Require it to separate observed facts from hypotheses. Review any proposed contract change against the implementation; do not loosen a schema just to make a failing check green.

Make one change travel through the workflow

Suppose the API renames projectId to id. Updating an example alone leaves several places to drift. Review the response schema, the captured-value step, the subsequent call, and the reference example together. Rerun the scenario with the new response and keep a separate compatibility case if existing clients still use the old field.

This is the workflow we are building around at Powerduck: a local OpenAPI document as the foundation for AI-assisted API work, MCP tools, debugging, documentation, and scenario testing. The benefit is being able to work across those activities around the same contract, rather than treating each successful request as the finish line.

Start small. One create-read-check scenario with trustworthy evidence is a better foundation for an agent than a large tool catalog whose outcomes nobody verifies.

References: OpenAPI specification ยท Powerduck