Generate realistic fake data

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 mentions the return envelope format (`{ data, meta }`), reproducibility via seed, and batching via count. However, it does not discuss side effects, permissions, or potential errors.

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?

Three sentences with no wasted words. The first sentence states the core function, followed by parameter usage guidance. Structure is front-loaded and efficient.

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?

Given the absence of an output schema, the description sufficiently explains the return format. It covers all parameters, the dependency on `list_generators`, and behavioral nuances (seed, batch). The tool is straightforward, so completeness is adequate.

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 parameters are already described in the schema (100% coverage), but the description adds valuable context: examples for `options` (e.g., `{"sex":"f"}`), clarification that `count` is for batch and `seed` for reproducibility, and references to generator descriptions. This goes beyond what the schema provides.

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 purpose: generating realistic synthetic data using a generator ID from `list_generators`. It uses specific verbs ('generate', 'pass') and distinguishes from the sibling tool by referencing the generator list.

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 explains when to use the tool (need synthetic data from a specific generator) and implicitly directs to `list_generators` for IDs. It does not explicitly state when not to use it, but the context is clear.

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