get_industry_metric

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions aggregation but doesn't specify how metrics are aggregated (e.g., sum, average, median) or any limitations like rate limits, data freshness, or error handling. For a tool with 5 parameters and no annotations, this leaves significant behavioral gaps, though it does hint at industry-wide scope.

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 extremely concise with two sentences that are front-loaded and waste-free. The first sentence states the core purpose, and the second adds practical context. Every word earns its place, making it easy to parse quickly without unnecessary elaboration.

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 5 parameters, no annotations, and no output schema, the description is incomplete. It covers the high-level purpose and usage hint but lacks details on aggregation behavior, return format, or error cases. For a tool of this complexity, more context is needed to fully guide an agent, though it meets a minimal viable threshold.

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 100%, so the schema fully documents all 5 parameters with clear descriptions and defaults. The description adds minimal value beyond the schema, only implying that parameters relate to industry aggregation without detailing semantics like how 'limit' affects results. Baseline 3 is appropriate since the schema does the heavy lifting, but the description doesn't compensate with additional insights.

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 action ('Get') and resource ('financial metric aggregated across all companies'), making the purpose understandable. It distinguishes from siblings like 'get_company_facts' by focusing on industry-wide aggregation rather than company-specific data. However, it doesn't specify the exact aggregation method (e.g., sum, average), leaving some ambiguity.

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 provides some guidance by stating 'Useful for industry comparisons,' which implies when to use this tool. However, it doesn't explicitly mention when NOT to use it or name alternatives like 'get_financial_metric' (which might be for individual companies). The context is implied but lacks explicit exclusions or comparisons to sibling tools.

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