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

No annotations are provided, so the description must carry the behavioral transparency burden. It does so by explaining the data source (demand_signals), the combination logic (bundling miss and tight_fit), and the output dimensions (scene, dimension band, category). The word '返す' (returns) implies a read-only operation. It does not mention sorting or limit behavior, but those are covered by the schema.

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 compact at two sentences: the first states what the tool does, the second states when to use it. Every clause contributes value, and there is no repetitive or filler content.

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?

The description gives a good high-level overview but lacks details on the return structure, sorting, or how 'miss' vs 'tight_fit' are defined. Since there is no output schema, the description alone is expected to explain what the agent will receive; it only partially does so. It is adequate but leaves gaps for a complex analytical tool.

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 baseline is 3. The description adds some context by linking outputs to scene/dimension/category and explicitly mentioning tight_fit, but it does not meaningfully deepen the understanding of the 'intent' or 'limit' parameters beyond their 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 clearly states the tool's function: it bundles 'miss' and 'tight_fit' signals from demand_signals and returns product gaps by scene, dimension, and category. This distinguishes it from sibling tools like summarize_demand_signals, which presumably summarizes signals rather than finding specific gaps.

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 concrete use cases: identifying Amazon listing candidates, in-house development candidates, and priority dimension data bands to collect. It does not explicitly state when not to use the tool or mention alternatives, but the context is clear enough for an agent to decide when this tool applies.

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