macro_germany_retail_sale_yearly

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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false. The description adds the source URL and return type (pandas DataFrame) but does not disclose any additional behavioral traits such as pagination, rate limits, or data freshness. It is consistent with annotations, so no 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 brief and front-loads the essential information: source, metric, URL, return value. It is slightly redundant in repeating the metric name in the return line, but overall every sentence serves a purpose and the structure is clean.

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 simple read-only nature and no parameters, the description is adequate. However, there is no output schema, so the description should more explicitly describe the DataFrame's structure (e.g., columns or index). It only names the metric without specifying the data shape or historical range.

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 parameter schema is trivially complete. With no parameters, the description need not elaborate on parameter meaning. The baseline of 4 applies because no parameter information is necessary.

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 (东方财富/Data Center), the geographic and metric scope (Germany real retail sales annual rate), and the return type (pandas DataFrame). It distinguishes from sibling tools like macro_germany_retail_sale_monthly by explicitly specifying 'yearly'.

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?

The description implies usage for retrieving Germany's real retail sales annual rate from Eastmoney, but provides no explicit guidance on when to use this tool versus alternatives (e.g., monthly data, other countries). No exclusions or alternative recommendations are stated.

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