stock_dzjy_sctj

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

Annotations already declare read-only, idempotent, and non-destructive behavior, so the description only needs to add extra context. It adds the source URL and the return type (pandas.DataFrame), which are useful but minimal. No additional behavioral traits (e.g., data freshness, pagination, or error conditions) are disclosed, but the safety profile is well-covered by annotations.

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 extremely concise: a single line naming the data source, the URL, and a return type annotation. Every element is purposeful, and it is front-loaded with the tool's identity. There is no redundancy or 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?

For a no-parameter read-only tool, the description covers the essential functional context: the source (East Money), the specific page, and the output type. The term '市场统计表' is somewhat vague regarding exact columns or time coverage, but given the simple interface and good annotations, the tool is adequately specified.

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 description carries no parameter documentation burden. The baseline for 0-parameter tools is 4, and no further parameter semantics 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 clearly identifies the resource as the East Money block trade market statistics table (东方财富网-数据中心-大宗交易-市场统计) and specifies the return type as a pandas DataFrame. However, it lacks an explicit verb like 'fetches' or 'returns' and does not explicitly differentiate from sibling block-trade tools such as stock_dzjy_mrtj or stock_dzjy_hygtj, though the '市场统计' scope provides some distinction.

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 its alternatives. It merely states the data source and output type without mentioning any relevant context, such as 'use this for overall block trade market summary' or 'for daily statistics use stock_dzjy_mrtj'.

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