industry_themes

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

With no annotations provided, the description carries the full disclosure burden and does reasonably well: it reveals the data dependency on industry_daily_collect, the multi-signal computation approach (correlation, clustering, momentum, capital flow), and the JSON return format. It stops short of 5 by not describing failure or staleness behavior when prerequisite data is missing, but the core behavioral traits are disclosed.

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?

Two dense sentences with zero filler: the first front-loads purpose and method, the second packs the prerequisite and return format into one clauseful sentence. Every clause earns its place, and the most decision-relevant information (what it computes) appears first.

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?

An output schema exists, all three parameters are optional with defaults and fully documented, and the description states the data prerequisite, so an agent has what it needs to invoke the tool correctly. The remaining gaps — failure semantics when industry_daily_collect has not been run, and placement among the theme sibling tools — are minor given the output schema and full parameter coverage.

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% — window, n_clusters, and corr_method all carry meaningful descriptions such as '收益率回看窗口(交易日)' and '相关系数类型: pearson/spearman/kendall'. The description's mentions of 相关性 and 聚类 implicitly map to corr_method and n_clusters but add nothing beyond the schema, so the baseline 3 applies.

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 opens with '行业相关性主线识别' — a specific verb and resource — and enumerates the exact computations (相关性/聚类/动量/资金流) performed on industry daily quotes to produce the market's current main themes. It is unambiguous about what the tool does, but it does not explicitly differentiate from the closely named siblings industry_themes_dcc and industry_themes_causality, so it stops short of a 5.

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 states an explicit, actionable prerequisite: '需要先运行 industry_daily_collect 采集数据' — the agent knows it must run the collector first, which is real usage context. However, it gives no guidance on when to prefer this tool over the closely related theme siblings (industry_themes_dcc, industry_themes_causality, industry_seasonal_corr), leaving the vs-alternatives question unanswered.

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