maritime_crew_demographics

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 describes the tool as analyzing and showing distributions, implying a read-only operation. However, it does not explicitly state that no data is modified or disclose any potential side effects or limitations. The context makes the behavior clear, but there is room for explicit safety statements.

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 well-structured with a summary line, dataset context, detailed Args list, and tips. It is concise yet thorough, with no extraneous information. The Args list is slightly long but necessary for clarity, earning a high but not perfect score.

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 tool has 8 parameters and no output schema, the description explains the output as 'JSON or text with demographic breakdown' and mentions 'fate sub-distribution'. However, it does not specify the exact structure or edge cases, leaving some ambiguity for the agent. More details on output format would improve completeness.

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%, but the description provides detailed explanations for all 8 parameters in the Args section, including defaults and examples. It goes beyond schema by giving concrete usage tips and expected values, fully compensating for the lack of schema descriptions.

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 begins with a clear verb ('Aggregate') and resource ('crew demographics'), and specifies the grouping dimensions (rank, origin, fate, decade, ship). This distinguishes it from sibling tools like maritime_crew_career or search_crew, which focus on individual records or different analyses.

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 'Tips for LLMs' section provides explicit guidance on when to use each group_by option and how to combine filters, e.g., using group_by='decade' for trends or combining rank and decade for sailor recruitment. While it does not explicitly mention when not to use the tool, the tips are clear and practical.

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