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

Annotations already declare read-only, idempotent, non-destructive behavior in a closed-world context. The description adds useful behavioral context by explaining that the result is an estimate with specific contributing factors, including network-wide customer history, duplicates, and rejected leads.

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 a single front-loaded sentence that states the estimate, its range, and the factors behind it. Every clause earns its place and there is no redundant text.

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 read-only tool with one required parameter and no output schema, the description sufficiently explains the return concept: a 0-100% estimate plus contributing factors. The main omission is any clarification of the order_id parameter, but the overall calling context is adequate.

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 description coverage is 0% for the sole order_id parameter, so the description must compensate. It only implies that one order is evaluated, without defining order_id format, constraints, or how the identifier is interpreted.

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 ('Estimate') and resource ('that one order will be delivered'), making clear it returns a predictive probability rather than raw order data. It also distinguishes itself from retrieval siblings like get_order by naming the prediction output and contributing factors.

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 use case is implied: estimate delivery probability for a single order. However, the description gives no explicit guidance on when to choose this over get_order, get_stats, or other siblings, and no when-not conditions are stated.

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