macro_canada_retail_rate_monthly

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

Annotations already declare readOnlyHint and idempotentHint, so the safety profile is known. The description adds that the return type is a pandas.DataFrame and provides a source URL, but does not reveal any edge cases, rate limits, or data quirks. No contradiction with annotations.

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 minimal, with three components: title, URL, and return type. It is not verbose, but the structure is more of a stub than a well-organized explanation, lacking any usage context.

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?

Given the tool's simplicity (no params, read-only, no output schema), the description is barely adequate. It mentions the metric and return type but omits details about the DataFrame columns, date range, or units, which would be valuable 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 zero parameters, so the schema provides complete coverage. The description correctly does not need to explain parameters, and the return type is mentioned.

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 source (Eastmoney), country (Canada), and metric (retail sales monthly rate), which distinguishes it from sibling macro tools. However, it lacks an explicit verb like 'fetches' or 'returns', relying on the title to convey the action.

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_canada_cpi or macro_australia_retail_rate_monthly. It simply states the topic and returns a DataFrame, leaving the agent to infer suitability.

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