API documentation completeness check

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You can use AI to run an API Documentation Completeness Check on your current API documentation and generate a detailed review report. Based on this report, you can refine and improve your documentation — making collaboration smoother, the docs easier to read, and all AI features more effective thanks to more accurate endpoint specs.

What Is Checked

Before running the API Documentation Completeness Check, it's helpful to review whether the descriptions and definitions in the current API documentation are complete. You can start from the following aspects:

1. Basic Definitions

Check whether the endpoint documentation clearly and accurately specifies:

Request Method: Defines the HTTP method of the endpoint (such as GET, POST, PUT, or DELETE), used to determine the semantics of the request, idempotency, and whether the input parameter structure is appropriate.

Request Path: The full URL path (excluding the base URL), used to identify the endpoint uniquely and infer the structure of path parameters.

Endpoint Name and Description: A short explanation of what the endpoint does. This helps both readers and AI understand the business logic, making it easier to assess whether parameters, responses, and error codes are complete and reasonable.

2. Parameter Descriptions

Check whether the endpoint documentation clearly and accurately specifies:

Parameter Name (if necessary, check Params, Body, Headers, Cookies, etc.): Used to ensure that the parameter structure is complete, naming is consistent, and each parameter is in the correct location.

Example: Provides valid sample input values to help understand typical values, formats, data types, and business meaning of the parameter.

Description: Explains the meaning, purpose, and business scenarios of the parameter.

The basic information of request parameters can be generated or modified with the help of AI.

3. Parameter Constraints

Check whether the endpoint documentation clearly and accurately specifies:

Required/Nullable: Clearly indicates whether a parameter is required and whether null values are allowed, which is used to check whether the test scenarios are reasonable(such as missing required fields or invalid null values).

Enums/Const: Lists the possible enum or constant values of the parameter and their meanings, so that the legality and coverage of parameter values can be verified.

Boundary Values: Provides minimum and maximum values, length limits, and other constraints, used to check whether the documentation correctly expresses the parameter's constraint conditions.

format: Describes any special format of the parameter (such as date-time, email, uuid, binary, int64, json-string, etc.), to ensure that the AI model can accurately validate format correctness.

4. Response

Check whether the API documentation clearly and accurately specifies:

Response Body: Provides the complete structure for the response's content type, explanations of each field, HTTP status codes, and error codes, so that the AI model can determine whether the endpoint output is standardized, whether any fields are missing, and whether the structure is consistent.

The basic information of responses can also be generated or modified with the help of AI.