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

Annotations already declare readOnly and idempotent, so the description adds real value beyond them by explaining the verdict semantics, relative difference, provenance, and the ambiguous-query fallback with candidate observations. The guidance to narrow time rather than picking a candidate is a meaningful behavioral disclosure.

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?

Every sentence earns its place: sources, when-to-use, required inputs, return semantics, and the edge case. The most important usage trigger is front-loaded, and the ambiguity fallback is explained efficiently at the end.

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?

Even without an output schema, the description covers purpose, invocation pattern, expected result, and a critical edge case with remediation. The input schema handles parameter formats, so nothing necessary for correct use 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 description coverage is 100%, so the baseline is 3. The description reinforces the expected-value parameter and the ambiguity issue around time, but it does not materially improve on the schema's own parameter descriptions. It also slightly implies expected is required before the schema clarifies it is optional for existence-only checks.

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 uses a specific verb ('Verify') and resource ('claimed value against the primary source'), and even enumerates the named sources. The explicit 'did you hallucinate that?' trigger clearly distinguishes this verification tool from data-retrieval and calculation siblings like get_entity_data or calculate.

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 tells the agent exactly when to use it: when a user questions a number or before presenting cited figures. It does not explicitly enumerate when not to use it or name sibling alternatives, but the context is clear enough that this is the verification/checking tool, not the general lookup tool.

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