macro_japan_core_cpi_yearly

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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, ensuring the agent knows this is a safe read operation. The description adds the data source (East Money) and return type (pandas.DataFrame), which provides context beyond the annotations without any contradiction.

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 concise and structured with a title, source URL, return value, and return type. Each line has minimal waste, though the URL is somewhat noisy and the return statement essentially repeats the title.

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?

With no output schema, the description should clarify return values. It provides the return type and a generic name for the data series, but it does not specify columns, date range, or frequency. For a simple no-param data retrieval tool, this is minimally viable but leaves some ambiguity about the DataFrame's structure.

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, so the schema coverage is 100% and there is no parameter ambiguity. The baseline of 4 applies, and the description correctly does not need to explain 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 identifies the data (Japan national core CPI yearly) via its title and return statement, and it distinguishes from sibling tools like macro_japan_cpi_yearly by specifying 'core'. However, it lacks an explicit action verb like 'fetch' or 'retrieve', relying on the docstring 'return' to imply the tool's behavior.

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 guidance is provided on when to use this tool versus alternatives. There is no mention of use cases, exclusions, or comparisons with other macro economic data tools, leaving the agent to infer usage solely from the tool name and title.

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