generate_kotlin_mock_factory

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

No annotations exist, so the description carries the full burden. It does disclose that the generated repositories are in-memory and simulated, which sets important expectations. But it does not mention file system side effects, overwrite behavior, or setup requirements beyond saying 'generates'.

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

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

The description is a single, front-loaded sentence with no wasted words. It efficiently lists the generated outputs and the purpose without redundancies, making it easy to parse.

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

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

The tool generates multiple artifact types and has three parameters, yet the description stays at a high level. It lacks detail on how fields and itemCount affect the output, what the generated code looks like, and how an agent should integrate it into a project. Without an output schema, more context is needed.

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

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

Schema coverage is only 33% (only domainName is documented), and the description adds no explanation for the 'fields' or 'itemCount' parameters. Though the schema's enum lists possible field types, the description does not compensate for the low coverage by explaining how to configure fields or control item count.

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

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

The description uses a specific verb ('Generates') and names concrete artifacts: mock data factories, fake entities, and in-memory simulated Flow repositories. It clearly ties the tool to 'instant hackathon UI prototyping' and distinguishes it from sibling tools that scaffold real backends or databases by emphasizing the mock/simulated nature.

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

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

The phrase 'for instant hackathon UI prototyping' provides a clear context for when to use the tool, and the mock/fake/simulated language implies it is not for production. However, it does not explicitly name alternatives or state when not to use it, so it stops short of a 5.

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