macro_stock_finance

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

Annotations already establish the read-only, idempotent, non-destructive profile, so the bar for additional disclosure is lower. The description adds the source URL and the pandas.DataFrame return format, which is useful context, but it reveals nothing about columns, time ranges, pagination, or data freshness. No contradiction with annotations exists.

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 and front-loaded with the data source label and URL. However, the line ':return: 股票筹资' largely restates the title, making it slightly redundant, while the rtype line adds genuine information.

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 no output schema, the description should explain the return value shape, but it only gives the topic (股票筹资) and container type. The DataFrame's columns, units, and temporal scope are unspecified, which is a notable gap, though the zero-parameter design keeps overall complexity low.

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 tool has zero parameters, so the input schema fully covers inputs and the baseline of 4 applies. The description's return-type note ('pandas.DataFrame') adds marginal value, and there are no undocumented parameters to compensate for.

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 labels the data resource (同花顺-数据中心-宏观数据-股票筹资, i.e., THS Data Center macro stock financing) and states that it returns a pandas DataFrame of 股票筹资 data, making the basic purpose evident. However, it lacks an explicit action verb and does not differentiate itself from sibling tools like macro_china_stock_market_cap or stock_financial_abstract.

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 is purely a data-source label with a URL and return type, with no mention of appropriate scenarios, exclusions, or preferred sibling tools.

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