macro_usa_core_cpi_monthly

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

Annotations already declare readOnlyHint, idempotentHint, destructiveHint=false and openWorldHint, so the safety profile is covered structurally. The description usefully adds the data coverage window (19700101-present) and the source URL, but says nothing about update cadence, latency, or how current the 'present' endpoint is.

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 core purpose and data range are front-loaded in the first sentence, with the source URL immediately after. The trailing ':return:' line is redundant since it merely restates the resource name, and the ':rtype:' docstring boilerplate is minor noise.

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 no-parameter, no-output-schema data retrieval tool, the description supplies the essential facts: what the dataset is, its full temporal coverage, its source, and the return type (pandas.DataFrame). Only a note on update frequency or column structure is missing.

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 to document and the baseline is 4. The description correctly implies a parameterless, full-history pull rather than any filtered query.

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 (美国核心 CPI 月率报告) with its temporal scope (19700101-至今), so an agent knows exactly what dataset this returns. The '核心' (core) qualifier implicitly distinguishes it from the headline macro_usa_cpi_monthly sibling, but this distinction is not made explicit.

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 the many closely-named siblings (macro_usa_cpi_monthly, macro_usa_cpi_yoy, macro_usa_core_ppi, macro_usa_core_pce_price). No exclusions or alternative routing are provided; the agent must infer usage purely from the name.

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