scout_market

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions the return values (market size, growth rate, etc.) but doesn't cover critical aspects like data sources, accuracy, rate limits, authentication needs, or whether this is a read-only operation. For a research tool with zero annotation coverage, this leaves significant gaps in understanding its behavior.

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 brief and front-loaded with the core purpose in the first sentence, followed by a clear list of return values. There's no wasted text, but it could be slightly more structured (e.g., separating purpose from returns with a colon or bullet points).

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's moderate complexity (researching markets), the presence of an output schema (which handles return values), and 100% schema coverage, the description is minimally adequate. However, it lacks context about data freshness, scope limitations, or how it differs from siblings, which would be helpful for an agent to use it effectively.

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?

The schema description coverage is 100%, so the input schema already fully documents the 'query' and 'depth' parameters. The description adds no additional parameter semantics beyond what's in the schema, such as examples of effective queries or implications of the 'depth' setting. This meets the baseline for high schema coverage.

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 as 'Research any market or industry' with a specific verb ('Research') and resource ('market or industry'), making it immediately understandable. However, it doesn't explicitly differentiate from sibling tools like 'scout_trends' or 'scout_competitors' which might have overlapping functionality, preventing a perfect score.

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 no guidance on when to use this tool versus alternatives like 'scout_trends' or 'scout_competitors' from the sibling list. It also lacks context about prerequisites, limitations, or typical use cases, leaving the agent to infer usage based on the tool name alone.

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