macro_china_pmi_yearly

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

Annotations already declare readOnlyHint=true, destructiveHint=false, idempotentHint=true, and openWorldHint=true, which cover the safety and side-effect profile. The description adds the data source URL and date range, but does not disclose other behavioral traits such as column details, missing data handling, or rate limits. This is similar to the get_calls example where annotations carry the main safety profile and the description adds some context.

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 key information, including the data type, date range, source URL, and return type. However, it repeats '中国年度 PMI 数据' twice (once at the beginning and once in the :return: line), which is slightly redundant but not overly verbose.

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 low-complexity tool with 0 parameters and no output schema, the description provides essential facts: data type, date range, source, and return type. However, it is ambiguous about the exact data content (e.g., whether it includes manufacturing vs. services PMI, what columns are present) and does not clarify the meaning of '年度' in the context of a date range starting 2005-02-01. This leaves some gaps for an agent.

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 0 parameters and an empty input schema, so there are no parameter semantics to explain. The baseline for 0 parameters is 4, and the description does not need to compensate for any undocumented parameters.

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 states it returns '中国年度 PMI 数据' (China annual PMI data) with a specific date range and a return type of pandas.DataFrame. It distinguishes from sibling tools like macro_china_pmi by specifying 'yearly' frequency, but does not explicitly say it is manufacturing PMI (though the URL hints at it) and does not directly compare with related PMI siblings.

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 macro_china_pmi, macro_china_cx_pmi_yearly, or other macro indicators. The description simply states what it returns without any context on appropriate use cases, prerequisites, or exclusions.

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