macro_uk_core_cpi_monthly

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, so the safety profile is covered. The description adds a return type (pandas.DataFrame) and source URL, but no further behavioral details like data frequency, date range, or potential caveats.

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 extremely concise, consisting of a title line, a URL, and a return type annotation. Every line earns its place, and the most important info (metric and source) is front-loaded.

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-param tool with no output schema, the description provides the essential context: the specific economic indicator (UK core CPI monthly rate), the source (East Money with URL), and the return format (pandas.DataFrame). It lacks details like units or column names, but these are not critical for basic selection.

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 there is nothing to explain. Schema coverage is 100% (empty object) and the baseline for 0 params is 4; the description does not need to add parameter semantics.

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 retrieving UK core consumer price index monthly rate data from East Money (东方财富). It specifies the exact metric (核心消费者物价指数月率) and includes the source URL, effectively distinguishing it from siblings like macro_uk_cpi_monthly by the 'core' qualifier.

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 such as macro_uk_core_cpi_yearly or macro_uk_cpi_monthly. The description simply names the metric and source, leaving the agent to infer usage context from the tool name alone.

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