板块研报面：获取概念或行业板块指定日期段内的研报观点

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. The description only states what the tool does ('获取...研报观点') without detailing behavioral traits like rate limits, authentication needs, error handling, or output format. For a tool with no annotations, this leaves significant gaps in understanding how the tool behaves in practice, such as whether it returns structured data or raw text.

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 a single, efficient sentence that directly states the tool's purpose without unnecessary words. It's front-loaded with the core functionality, making it easy to parse. However, it could be slightly more structured by explicitly mentioning key parameters or usage context, but it avoids redundancy and 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 that an output schema exists (implied by 'Has output schema: true'), the description doesn't need to explain return values. However, with no annotations and a tool that likely returns complex data (research report viewpoints), the description is minimal and lacks context about the tool's scope, such as data sources or limitations. It's adequate as a basic summary but incomplete for guiding an AI agent in complex scenarios without additional structured data.

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 input schema fully documents all four parameters (query, startDate, endDate, maxCnt) with descriptions. The description adds no additional meaning beyond what the schema provides, as it doesn't explain parameter interactions, formatting examples (e.g., date format), or constraints. With high schema coverage, the baseline score of 3 is appropriate, as the description doesn't compensate but also doesn't detract.

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?

Tautological: description restates name/title.

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 no guidance on when to use this tool versus alternatives. It doesn't mention any prerequisites, exclusions, or comparisons to sibling tools such as 'sectorNewsAnalysis' or 'sectorFunAnalysis'. Without this context, an AI agent must infer usage based on the tool name and parameters alone, which is insufficient for optimal tool selection.

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