stock_gpzy_distribute_statistics_company_em

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

Annotations already declare this is read-only, idempotent, and non-destructive. The description adds the return type (pandas.DataFrame) and source URL, but does not reveal additional behavioral traits such as data freshness, pagination, or network dependencies.

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 very concise and well-structured, front-loading the title and including useful metadata (URL and return type). It loses a point for repeating '质押机构分布统计-证券公司' twice, which is slightly redundant.

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 simple no-parameter, read-only tool, the description provides sufficient context: it states the source, the specific data topic, and the return type. While it lacks column details or examples, the annotations and simplicity of the tool make this adequate.

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 schema fully covers parameter semantics. The description does not need to add parameter details; the baseline of 4 applies.

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: pledge institution distribution statistics for securities companies, with an explicit source URL. It distinguishes from sibling tools like the bank variant by specifying '证券公司' (securities companies), though it lacks an explicit verb like 'get' or 'retrieve'.

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 implies its usage through the specific dataset name and URL, making it clear this is for securities company pledge distribution stats. However, it provides no explicit when-to-use guidance or comparisons to alternative tools in the same family.

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