spot_price_qh

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

Annotations already declare readOnlyHint=true, destructiveHint=false, and idempotentHint=true, so the safety profile is covered. The description adds that it returns a pandas DataFrame and includes the source URL, but it does not disclose data shape, frequency, or other behavioral caveats.

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 brief and front-loaded with the title and URL, but includes raw docstring syntax that adds minor noise. Every element conveys some information, and there is no significant verbosity.

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?

With only one parameter and no output schema, the description gives the essential input but the return is only described as '现货走势' with type pandas.DataFrame, lacking column details, time range, examples, or any constraints. This leaves the agent uncertain about the exact output structure.

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 only provides a default value for symbol, with no description (0% coverage). The description compensates by stating ':param symbol: 品种名称' (commodity name), clarifying the parameter's meaning, but it does not enumerate valid values or format.

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 identifies the tool as '99 期货-数据-期现-现货走势' with a source URL, indicating it provides spot trend data for commodities from 99qh.com. It specifies the parameter (symbol) and return type (pandas DataFrame), but uses no explicit action verb and does not differentiate from similar futures/spot tools.

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 versus alternatives like futures_spot_price or spot_price_table_qh. The description only lists input and output, providing no context for selection or exclusion.

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