industry_overview

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

With no annotations, the description fully details that the tool returns aggregated statistics and is a read-only query. It specifies the output structure (quartiles, distributions, top apps) but does not discuss permissions or error behavior. However, for a simple query tool, this is adequate.

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 concise and well-structured: it opens with a summary, lists return fields, and provides usage examples plus parameter details. Every sentence adds value without redundancy.

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 low complexity (one parameter) and presence of output schema, the description fully covers purpose, output, parameter semantics, and usage examples. No gaps remain.

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%, but the description explicitly lists all allowed industry values (Apparel, Beauty, etc.) in the args section. This compensates fully for the missing schema descriptions, adding clear meaning to the parameter.

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 it provides 'big-picture stats for one industry vertical' and enumerates specific return fields (store count, median product count, etc.). It distinctively differs from sibling tools like 'analyze_store' or 'app_adoption' by focusing on aggregate industry-level data.

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 example questions ('what does the Beauty vertical look like?') indicating when to use. It does not explicitly state when not to use or mention alternatives, but the examples provide clear usage context.

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