macro_china_cx_pmi_yearly

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

Annotations already declare readOnlyHint=true, idempotentHint=true, destructiveHint=false and openWorldHint=true, covering the safety profile. The description adds the historical coverage window and the upstream data source, plus the return type (pandas.DataFrame), which is modest but genuine added context beyond the structured fields.

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?

Front-loaded with the dataset and date range, which is the essential information. The final ':return:' line restates the purpose verbatim and is pure redundancy, and the raw URL is arguably metadata rather than description text, but it is short overall.

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 zero-argument, read-only time-series endpoint whose annotations already carry the safety profile, the description supplies the two things an agent needs to call it correctly: what the data is and how far back it goes. It is not quite complete because return shape and update cadence are left implicit.

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 takes zero parameters, so there is nothing for the description to clarify and the baseline is 4. No parameter semantics are missing.

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?

States a specific resource (China Caixin PMI data) plus a temporal scope (20120120–present) and a source URL, so the agent knows exactly what dataset it gets. However, it does not disambiguate from the sibling macro_china_cx_services_pmi_yearly: the name implies manufacturing but the description never says so, leaving some sibling ambiguity.

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 when-to-use guidance and no alternatives are named, even though the sibling list contains several closely related PMI tools (yearly, services, non-manufacturing). The agent must infer applicability from the name alone.

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