stock_institute_recommend

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

The annotations already declare readOnlyHint=true, openWorldHint=true, idempotentHint=true, and destructiveHint=false, covering safety. The description adds the source URL and the fact that it returns a pandas DataFrame, which is useful but does not go into deeper behavioral details such as data freshness, pagination, or response size. Since annotations carry the safety burden, a score of 3 is appropriate.

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 the source URL, parameter documentation, and return type, all in a standard format. The list of symbol choices is somewhat long but necessary and does not add excessive verbosity. It is efficient and front-loaded with the tool's purpose.

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 tool with a single parameter and no output schema, the description provides sufficient context: the data source, the valid parameter choices, and the return type (pandas DataFrame). Combined with the default value in the schema, an agent can reasonably invoke the tool. It is not perfect because it lacks examples or explanation of the default behavior, but it is adequate for simple read-only data retrieval.

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 only defines 'symbol' as a string with a default, but the description enriches it with an explicit list of valid choices: '最新投资评级', '上调评级股票', '下调评级股票', '股票综合评级', '首次评级股票', '目标涨幅排名', '机构关注度', '行业关注度', and '投资评级选股'. This adds significant meaning beyond the bare schema, though it does not explain what each choice returns in detail.

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 names the resource '新浪财经-机构推荐池-最新投资评级' and includes the source URL, making it clear this tool retrieves latest investment ratings from Sina Finance's institutional recommendation pool. However, it does not explicitly distinguish itself from closely related siblings like stock_institute_recommend_detail or stock_institute_hold, so it falls short of a 5.

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 only lists the symbol choices and return type, but does not explain use cases, prerequisites, or situations where another tool (e.g., stock_institute_recommend_detail) would be more appropriate. This is a clear gap.

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