stock_institute_hold

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

Annotations already declare readOnlyHint, idempotentHint, and non-destructive behavior, so the safety profile is covered. The description adds useful context: data availability starts from 2005, the quarter encoding scheme, and the return type (pandas.DataFrame), but does not disclose pagination, rate limits, or the actual columns/scope of the returned table.

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-loads the main purpose, followed by the URL and docstring-style parameter/return notes. The URL may be unnecessary for an agent, and the return line simply repeats the tool name, but overall every sentence carries useful 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?

For a one-parameter, read-only tool, the description covers invocation and parameter semantics well. However, with no output schema, it does not describe the DataFrame's columns, row semantics, or which market/scope of stocks is covered, leaving the agent without a clear picture of the result 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 schema has no parameter descriptions (0% coverage), but the description fully compensates by explaining the symbol format, the quarter-to-code mapping, and providing concrete examples like '20191' and '20193'. It also specifies the type as str, making the single parameter unambiguous.

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 tool as returning a Sina Finance institutional holdings overview table (机构持股一览表) and specifies the data source URL. However, it lacks an explicit verb like 'get' or 'fetch', and while the name suggests it is the overview variant, it does not explicitly contrast with sibling tools like stock_institute_hold_detail.

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?

No guidance is given on when to use this tool versus alternatives such as stock_institute_hold_detail or stock_institute_recommend. The description only explains the symbol parameter format, leaving the agent to infer the tool's contextual role among many stock data tools.

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