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api_create_mock

Generate a mock API server from an OpenAPI spec or route definitions, returning realistic fake data that matches your API schema.

Instructions

Generate a mock API server that returns realistic fake data matching your API schema

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
portNoPort for mock server
api_keyNo
spec_pathYesPath to OpenAPI spec or route definitions
Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries the full burden of behavioral disclosure, and it does not carry it far. The description does not reveal that an api_key parameter is required for operation (implying an external or authenticated service), whether the tool writes files to disk, launches a long-running process, or calls a remote API — all significant behavioral traits. The 'realistic fake data' phrase is the only behavioral detail beyond the name, leaving the agent blind to side effects.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single 16-word sentence with no fluff — the action is front-loaded and every word earns its place. It is appropriately terse, though the structure could have been improved by adding a second sentence covering usage or behavioral context without sacrificing brevity.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a tool with no annotations and no output schema, the description must compensate, and this one does not. An agent cannot determine what artifact the tool produces (server code vs. running process), why api_key is needed, how the tool relates to the many data-generation siblings, or what happens on invocation. The port parameter (default 4000) hints at a live local server, but that inference is left to the agent rather than confirmed by the description.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters2/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 67%, so port and spec_path are documented in the schema, but api_key is entirely undocumented in both the schema and the description. The description's phrase 'matching your API schema' loosely reinforces the role of spec_path, but it adds nothing about why a key is needed, whether it is optional, or what it authorizes. The one ambiguous parameter is left unexplained, which the description should have compensated for.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description states a specific verb ('Generate'), a clear resource ('mock API server'), and the defining behavior ('returns realistic fake data matching your API schema'). It is not a tautology and gives an agent a concrete sense of the deliverable. However, it does not differentiate itself from closely related siblings like data_generate_from_schema or openapi_generate_server, leaving some ambiguity about whether the output is a running server, generated code, or a hosted endpoint.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides no guidance on when to use this tool versus alternatives. Sibling tools such as data_generate_from_schema (generate data), data_generate_realistic (realistic data), and openapi_generate_server (generate a server from OpenAPI) overlap conceptually, and nothing in the description tells an agent that it should pick api_create_mock when it wants a runnable mock endpoint rather than a data dump or production server scaffold. The agent must guess the use case from the tool name alone.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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