stock_zh_a_spot_tx

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint, covering safety and mutability. The description adds the return type (pandas.DataFrame), the specific source (Tencent), and the scope (all stocks for SH/SZ/BJ), which provides some context beyond annotations. It does not contradict annotations and offers a modest behavioral addition, but no deeper details like rate limits or data freshness.

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 compact: a clear title line, a source URL, and standard return/rtype annotations. It avoids bloat and front-loads the key information. The URL adds verifiable provenance but is not strictly necessary; still, the overall structure is efficient and scannable.

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?

For a zero-parameter retrieval tool, the description covers the essential elements: data source, market coverage, data type (real-time), and return format. With annotations covering safety and no output schema present, this is sufficient for an agent to correctly invoke and interpret the result. Minor gaps like specific columns or update frequency remain, but they are not critical for selection.

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?

The input schema is an empty object with zero parameters, so there are no parameter semantics to describe. The description correctly implies no inputs are required, matching the 100% schema coverage. Per rubric, 0 params yields a baseline of 4; no additional param details are needed.

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 "腾讯证券-沪深京-实时行情数据" (Tencent Securities - SH/SZ/BJ - real-time quotes) and "所有股票的实时行情数据" (real-time data for all stocks), clearly identifying the source (Tencent), market coverage (Shanghai, Shenzhen, Beijing), and data type. This distinguishes it from sibling tools like stock_zh_a_spot_em (Eastmoney) and stock_zh_a_spot (other sources) via the explicit Tencent source.

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?

There is no guidance on when to use this tool over alternatives. The description only explains what it returns (all-stock real-time quotes) but does not mention scenarios, exclusions, or compare with other spot data tools. Unlike the high-scoring example, it names no alternative tools or conditions.

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