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

Annotations already declare read-only, idempotent, non-destructive behavior, and the description adds contextual transparency by specifying the data is seasonally adjusted, from the Census Bureau's Advance Monthly Retail Trade Survey, and a monthly government release. It does not contradict annotations and provides useful processing details (MoM and YoY calculations).

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 well-organized and concise: first sentence defines the metric, second explains the return, then usage guidance, then args, and a sample output. Every sentence serves a purpose, no wasted words, and it is front-loaded with the most critical information.

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 simple, zero-parameter tool, the description is fully complete. It explains what is returned (sales total, MoM, YoY, source) and provides an example of the structured content, even without an output schema. It also covers usage context and limitations, making it entirely sufficient for an agent to invoke correctly.

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, and the description explicitly states 'Args: none.' This is unambiguous and clearly communicates that no input is required. The baseline for 0 params is 4, but the explicit statement earns a 5.

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 states the tool provides 'Latest U.S. retail sales, seasonally adjusted, excluding motor vehicles and parts' with a specific scope (ex-autos) and intent (consumer-spending signal). This distinguishes it from other macro tools and establishes the exact resource and output.

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?

Includes explicit 'When to use' and 'When NOT to use' sections, listing appropriate use cases (consumer spending, GDP nowcasting, retail demand) and exclusions (category-level detail, auto-inclusive headline, real-time/weekly data). This provides clear guidance for tool selection versus alternatives.

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