industry_themes_causality

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

With no annotations, the description carries the burden of behavioral disclosure. It discloses significant behavioral traits: slower computation (~60s), dependency on statsmodels, and JSON return format. These go beyond the basic function and help the agent set expectations. However, it doesn't mention potential side effects or data requirements, but for an analysis tool this is reasonably transparent.

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 highly concise: two sentences with critical information front-loaded (purpose, then performance/dependency notes). Every sentence earns its place, covering function, limitations, and output format with no waste.

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 has an output schema and only 2 optional parameters with full schema coverage, the description is largely complete. It covers purpose, output format, and performance caveats. It does not explain the output schema's structure, but the output schema itself is available, so the description needn't. Minor gap: no mention of required input data context, but this is acceptable for a compute 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%, with both 'window' and 'max_lag' explicitly described. The description itself doesn't add parameter-level meaning beyond what the schema provides, though it mentions '收益率回看窗口' indirectly aligning with window. Baseline 3 is appropriate when the schema already documents parameters fully.

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 states a specific verb ('找出'/'识别') and resource ('Granger因果检验 + 龙头行业识别'), clearly indicating the tool identifies lead/lag industries and causal transmission chains. It is distinguishable from siblings like industry_themes and industry_themes_dcc by the explicit causal analysis focus, though it doesn't explicitly name those siblings.

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 implies usage for analyzing causal relationships between industries, contrasted implicitly with other industry tools. It does not state when to use this vs alternatives, nor any exclusion criteria, but the focused purpose gives some contextual guidance. The '计算较慢' warning also hints at usage timing, but no explicit when/when-not guidance is provided.

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