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

Annotations already mark this read-only and non-destructive, and the description adds the return contract: a confidence-scored label with supporting signals and price history. It could disclose limitations around prediction reliability, but it does not contradict the annotations and provides meaningful behavioral context.

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 two sentences that front-load the purpose, then state the use case and the return value. Every sentence earns its place, and there is no filler or redundant repetition of the tool name.

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 prediction tool with a full input schema and an output schema, the description covers the core purpose, the decision context, and the nature of the response. Nothing essential for an agent to call this tool correctly 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?

The input schema already documents all parameters with 100% coverage, so the baseline is 3. The description adds context about the nightly_cash_cost being the current rate to evaluate, but it does not need to add more since the schema handles parameter semantics well.

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 what the tool does: predicts whether a current hotel rate is a good deal using historical price trends. It is specific and actionable, but it does not explicitly differentiate itself from similar sibling tools like diagnose_rates or get_rate_alerts, leaving some room for ambiguity.

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 explicitly says to use this when the user is debating booking now versus waiting, which gives a clear trigger condition. It does not mention when not to use it or name alternatives, but the stated use case is specific enough to guide selection.

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