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

Annotations already flag read-only, non-destructive behavior. The description adds what the call returns and the comparison basis ("historical observations"), which is useful context beyond the annotations. There is 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?

Two sentences, with the core purpose up front, an illustrative example in the middle, and a practical use case at the end. Every sentence earns its place.

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 read-only 4-parameter tool with a full input schema and an output schema, the description gives enough context: what it computes, an example interpretation, and the intended use. No crucial invocation detail is missing.

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?

Schema coverage is 100%, so the schema already documents all four parameters. The description references "current rate" but adds no parameter-specific detail beyond the schema, so the baseline 3 is appropriate.

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 uses a specific verb+resource pair: "Get pricing percentile context for a specific hotel," and clarifies with a concrete example of what the percentile means. It is distinct from siblings like predict_price or diagnose_rates because it emphasizes historical percentile context rather than forecasting or diagnosing.

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?

It gives an explicit usage directive: "Use this to add objective context to recommendations." It does not name alternative tools or state exclusions, so it does not fully meet the when-not-to-use bar, but the intended context is clear.

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