stock_comment_detail_zlkp_jgcyd_em

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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the safety profile is covered. The description adds the return type (pandas.DataFrame) and the data source URL, but does not disclose behavioral details such as network dependency, potential website changes, or response format caveats. This does not contradict annotations, but adds limited value beyond them.

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 a compact docstring with clear sections (source, URL, param, return, rtype). The first line repeats the title already present in the annotations, which is mildly redundant, but overall the description is efficiently structured and front-loaded with the key identity.

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 one-parameter read tool, the description covers the essential parameter and return type, but lacks detail about the DataFrame columns, index, or the nature of the data (e.g., historical vs current). There is no output schema to compensate, so the description should provide more context about what the returned data represents.

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 provides zero description coverage, but the description compensates by documenting the sole parameter: ':param symbol: 股票代码' with type str. This gives meaningful semantics beyond the bare schema. It could be more precise about the expected format (e.g., 6-digit code, leading zeros), but the default value '600000' provides a concrete example.

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 data source (东方财富网), the specific data category (主力控盘-机构参与度), and the return type, making it clear this tool retrieves institutional participation data for a stock. However, it lacks an explicit action verb like 'get' or 'fetch', and does not directly differentiate itself from sibling tools beyond the feature name in the tool name.

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 such as stock_comment_detail_scrd_desire_em or stock_comment_em. The description only provides the source URL and parameter documentation, leaving the agent to infer usage context from the tool name alone.

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