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

With no annotations, the description must supply behavioral context. It adds useful semantics by explaining that the expression is a multi-factor intersection and listing common factor fields. However, it does not disclose side effects, permissions, rate limits, error behavior, or return characteristics.

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 front-loaded with purpose, then gives a compact example, a useful field list, and a user-intent trigger. There is no filler or unnecessary repetition of schema content; each clause contributes.

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 fully documented schema and the presence of an output schema, the description covers the remaining needs for correct invocation: expression syntax, valid factor fields, and the exact query context. An agent can select and call this tool confidently.

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 coverage is 100%, so the baseline is 3; the description adds value by explaining the multi-factor intersection semantics, giving a concrete example, and enumerating valid factor fields. It does not elaborate on limit/orderby, but the schema already documents those.

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 opening '因子选股（量化筛股）' names a specific operation and resource (factor-based A-share screening), and the closing '筛选/选股/符合条件的股票' maps it to user intent. It does not explicitly contrast with sibling tools such as ashare_search, so differentiation is clear but not fully articulated.

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 when-to-use condition: when the user asks to '筛选/选股/符合条件的股票' (screen/select stocks matching criteria). It provides no when-not-to-use guidance or named alternatives, which prevents a 5.

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