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

Annotations already mark the tool read-only and idempotent, and the description adds meaningful behavior beyond that: it answers the question, returns an auditable receipt, and returns needsClarification for ambiguous dates instead of guessing. No contradiction with annotations.

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, front-loaded with the core behavior, and every clause contributes information. No redundant restatement of the title or schema.

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 output schema exists and annotations cover the safety profile, the description supplies the remaining decision-relevant behavior: it answers, receipts, and clarifies ambiguous input. Nothing essential is missing for a correct call.

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 covers all three parameters at 100%, so the baseline is 3. The example query hints at q and tz usage, but the description does not materially expand on the schema's parameter documentation.

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?

Description states a clear verb and resource: it parses natural-language date questions into a normalized operation, answers them, and returns an auditable receipt. It also distinguishes itself from arithmetic siblings by explicitly covering ambiguity handling (needsClarification) and natural-language input.

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?

Usage context is clear: this tool is for natural-language date questions, with a concrete example and explicit ambiguity behavior. However, it never says when not to use it or names alternatives like business_days or days_between, so it lacks full routing guidance.

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