scan_market

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

With no annotations, the description carries the full burden of behavior. It usefully discloses the conditional output and the no-data fallback plan. However, it doesn't explain where the data comes from, what 'has data' means, or what the template/authoritative sources look like.

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?

One compact sentence with two clear branches and no filler; the core behavior is front-loaded. It earns its length even though more detail would be welcome.

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?

For a tool without annotations or an output schema, this is incomplete: it omits the template format, the nature of the retrieval plan, how region and category shape the scan, and what counts as 'data.' An agent would need additional inference to call it correctly.

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%, and the description doesn't define region or category. It only hints that the presence of input data governs the output via '有数据则...无数据...', which likely maps to provided_data but never explicitly names it.

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 identifies a market-scan operation and states the two possible outcomes: structured template output when data exists, or a retrieval plan with authoritative sources and queries when it does not. This is specific about behavior, though it doesn't explicitly contrast with siblings like web_search or research_company.

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?

There is no guidance about when to prefer scan_market over web_search, research_company, or other sibling tools. The data-dependent branch describes internal behavior, not the circumstances that should lead an agent to select this tool.

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