generate_test_data

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

With no annotations, the description carries the full burden, and it does disclose two important traits: values are obviously fake (safe for non-production use) and check-digit-valid (they will pass validation). It says nothing about determinism behavior, whether the operation writes or reads, or rate/identity constraints.

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?

A single tight sentence with the most important qualifier ('clearly FAKE') front-loaded. Nothing is wasted, though it is arguably too short given how much parameter explanation is missing.

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?

No output schema, no annotations, and 0% parameter documentation mean the description should do more. It never explains what is returned (a list of 'count' values?), how 'seed' affects reproducibility, or which document types are valid.

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 description coverage is 0%, so the description must compensate and largely does not. 'For a document type' weakly gestures at the required 'type' parameter, but 'seed' and 'count' are entirely unexplained in both schema and description, leaving determinism and result-size semantics opaque.

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?

States a specific verb (Generate) and resource (test values for a document type), and the qualifiers 'clearly FAKE' and 'check-digit-valid' pin down the nature of the output precisely. It is distinguishable from the validate_* and list_document_types siblings, though it never names them.

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?

There is no explicit when-to-use or when-not guidance, and no named alternatives. The agent must infer that this is the counterpart to validate_document when a testing scenario needs synthetic data, which is a reasonable but unstated inference.

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