stock_history_dividend

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

Annotations already declare readOnlyHint=true and destructiveHint=false, so the agent knows it's a safe read. The description adds the source URL and the scope '所有股票的历史分红数据' (all stocks' historical dividend data), which clarifies the data scope. However, it doesn't disclose potential pitfalls like rate limits, data freshness, or the fact that it returns ALL stocks (which could be a large dataset). For a tool with annotations covering safety, this is acceptable but not rich.

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 short and front-loaded with the title, but it includes a raw URL that takes up space without explaining its relevance to the agent. The docstring-style 'return' and 'rtype' lines are succinct but add minimal value beyond the description. It's efficient but slightly cluttered with the URL and lacks a clean separation of purpose and usage.

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?

The tool is simple (zero params) and annotations cover safety, so the description doesn't need much. However, it doesn't describe the output schema or columns, which would help the agent understand what data comes back. Given the existence of sibling tools like stock_history_dividend_detail, it would be beneficial to clarify whether this returns aggregate or detailed data. The description mentions 'all stocks', but the agent might need to know if it can filter by stock or date. It's adequate for a zero-param tool but leaves some ambiguity.

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, and the input schema is empty with 100% coverage. Since there are no parameters to document, the description doesn't need to explain any. The baseline for zero parameters is 4, and the description correctly indicates no input is needed beyond calling the function. It doesn't add detail about return columns, but that's not required for parameter semantics.

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 states the source (新浪财经-发行与分配-历史分红), provides a URL, and indicates the return type (all stocks' historical dividend data as a pandas DataFrame). It clearly identifies the tool as a read-only data retrieval for dividend history. However, it lacks a comparison to sibling tools like stock_history_dividend_detail, stock_dividend_cninfo, or fund_fh_em, so it doesn't fully distinguish itself from similar dividend-related functions.

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 explicit guidance on when to use this tool versus alternatives. It doesn't mention prerequisites (e.g., needing a specific stock code) or scenarios where this tool is preferred over stock_history_dividend_detail or stock_dividend_cninfo. The description only states what it does, not when to use it.

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