问财选A股（同花顺问财）

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

With no annotations provided, the description carries the burden of behavioral disclosure. It does disclose the data source (同花顺问财) and that the tool performs natural-language screening across multiple condition types. However, it does not mention limitations, failure modes, pagination behavior, or whether the result is a list of securities, which would improve transparency.

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 dense sentence with a source URL appended. Every element is useful: the operation, the input style, the supported condition types, and the data provenance. No redundant filler.

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?

The tool is simple: one required string parameter and two optional pagination parameters. Thedescription plus schema covers what the tool does and what to pass. It lacks a stated return format, but 'A 股筛选' strongly implies a list of stocks, so the gap is minor.

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 schema already documents query, page, and limit. The description confirms the query is a natural-language question but adds no format examples, default behavior, or nuance beyond what the schema provides. Baseline 3 is appropriate.

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 states the tool's purpose: screening A-shares via natural language queries, with explicit mention of supported condition types (market indicators, technical patterns, financial metrics, industry concepts). This distinguishes it from sibling tools like select_hkstock and select_usstock by focusing on the A-share market.

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 intended use case is clear: screen A-shares using natural language. However, it does not explicitly explain when to choose this tool over alternatives such as select_etf or query_* data tools. The distinction is implied rather than stated.

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