schema_generate_mock

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

No annotations are provided, so the description carries the full burden. It clarifies the output (mock JSON data) but does not mention any side effects, permissions, or whether the operation is read-only. The description gives a functional overview without explicit behavioral details.

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, concise sentence that includes the core purpose and example data types. It is well-structured and front-loaded with the main action, 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 description is complete for the tool's purpose, covering input formats and output type. It mentions 'relational' to hint at relationship handling, but does not elaborate on output structure. Given the lack of an output schema, this is sufficient but not overly detailed.

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?

All three parameters are described with sufficient detail: count specifies range and default, entityName clarifies optionality, and schemaInput explains the accepted formats. Coverage is 100% and the descriptions add meaningful context.

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 clearly states the tool's function: generating realistic, relational synthetic mock JSON data. It identifies the input resource (SQL, Prisma, or JSON schema) and distinguishes it from sibling tools like schema_validate and schema_convert by focusing on generation.

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 description implicitly indicates use when mock data is needed from a schema, but it does not explicitly contrast with when to use schema_validate or schema_convert. No direct 'use this for X, not for Y' guidance is provided.

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