data_sample_data_generator

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 generating datasets but does not disclose behavioral traits such as whether data is transient, any authentication needs, or rate limits. It implies a read-only operation (demo data), but this is not explicit.

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 short and front-loaded with the purpose. However, it includes redundant 'Menu ID: sample_data_generator' which adds no value. Otherwise, it is 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 complexity of generating different dataset shapes and no output schema, the description is somewhat complete but relies heavily on 'describe_tool' for full guidance. It mentions the output formats but not the structure of the returned data, which is a gap.

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 0%, so the description must compensate. It adds value by listing examples for 'shape' (users, orders, etc.) and mentioning format options (JSON, NDJSON, CSV, TSV). However, 'count' is not described, and the exact meaning of 'shape' is still vague without additional 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 generates curated demo datasets with specific shapes (users, orders, etc.) and formats (JSON, NDJSON, CSV, TSV). It distinguishes from similar tools like 'data_random_data_generator' and 'data_data_faker' by mentioning curation and specific examples.

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 directs users to 'describe_tool' for full guidance, implying the current description is incomplete. It does not explicitly state when to use this tool over siblings, nor when not to use it. The mention of 'curated demo datasets' hints at usage but lacks clear context.

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