industry_seasonal_corr

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

With no annotations, the description carries the behavioral burden and does disclose key behaviors: it returns JSON, it requires pre-collected data, and it performs year/month-sliced correlation comparisons. It does not explicitly state side effects, but the analysis framing implies read-only behavior, and no contradictions exist.

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 three compact sentences: purpose and methodology, prerequisite, and return format. It is front-loaded and every sentence contributes actionable information.

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 read-style analysis tool with a complete input schema and an output schema, the description covers the essential operational context: what to pass, the minimum number of industries, the prerequisite data availability, and the JSON return. It could be more explicit about what happens when prerequisite data is missing, but that is a minor gap.

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 reinforces the industries parameter ('选择2个及以上板块') but adds no new detail about min_years or corr_method beyond the schema defaults and 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 identifies the operation as industry seasonal correlation analysis with a specific methodology (year- and month-sliced comparison across 2+ sectors) and a clear outcome (identifying which months have strongest/weakest linkage). It does not explicitly contrast this with sibling tools, though the prerequisite reference to industry_daily_collect helps separate data collection from analysis.

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?

It gives a concrete prerequisite: run industry_daily_collect first to collect data, and it states the input requirement of at least 2 sectors. This makes the invocation context clear, but it does not spell out when-not-to-use or name alternative analysis tools.

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