amac_manager_cancelled_info

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

Annotations already declare readOnlyHint=true, destructiveHint=false, and idempotentHint=true, establishing a safe read-only operation. The description adds context beyond annotations by specifying the data source URL, the meaning of cancellation type codes (100/200/300), and the return type. This enriches behavioral understanding without contradicting annotations.

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 block containing the official title, URL, category codes, and return type. It is front-loaded with the title and avoids excessive verbosity. However, the numeric codes (100/200/300) are cryptic without additional explanation, and the layout is a bit dense, preventing a perfect score.

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 covers essential aspects: the data source, the three cancellation categories, and the return type. It does not list the DataFrame columns or any pagination details, but the low complexity and strong annotations make this sufficient. Slightly more detail on output columns would improve completeness.

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 schema is effectively complete with an empty properties object. The description adds value by specifying the return type (:rtype: pandas.DataFrame), which helps the agent anticipate the output format. Since there are no parameters to explain, the baseline of 4 is appropriate.

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 the list of cancelled private fund managers from AMAC, with a specific source URL and return type (pandas.DataFrame). The verb '已注销' (cancelled) and resource '私募基金管理人名单' make the purpose unambiguous. However, it does not explicitly distinguish this from sibling AMAC tools like amac_manager_info, which limits differentiation.

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 usage for fetching cancelled manager data through its title and categories (主动注销, 依公告注销, 协会注销), but it does not state when to use this tool versus alternatives or provide exclusions. There is no explicit when-to-use guidance, but the context is clear enough for a user seeking this specific dataset.

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