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

With no annotations provided, the description carries the full disclosure burden and does well: it reveals the external side effect (emails a report), the scope of external interaction (queries four named AI platforms), and the safety boundary around email ownership. It doesn't mention potential latency or that this creates external third-party interactions beyond email, but the core behavioral traits are disclosed.

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?

Three sentences, each earning its place: the first explains the mechanism and deliverable, the second and third give the required consent and anti-speculation guardrails. The most important information is front-loaded, and there is zero filler.

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?

The description substitutes well for a missing output schema by telling the agent what the report contains (mention rate, citation rate, competitors), and it fully covers the safety-critical consent condition. The only gap is not positioning this against audit_ai_readiness, but everything needed to call the tool correctly is present.

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 schema already documents all five parameters meaningfully, which sets the baseline at 3. The description reinforces the consent/email relationship ('do not submit an address that is not theirs') and the purpose of notes (competitors to compare), adding marginal value, but it doesn't carry the parameter documentation burden.

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 states a specific verb and resource: 'Requests the free human-readable audit' and details exactly what the tool does (asks buyer questions to ChatGPT, Perplexity, Gemini and Claude, emails a report on mention rate, citation rate, and competitor mentions). This clearly distinguishes it from siblings like list_agentbuilt_tools and audit_ai_readiness by describing the concrete deliverable.

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 gives explicit when-to-call conditions: 'Only call this when the person you are acting for has asked for it and has given you their own email address' and explicit when-not-to-call rules ('Do not call it speculatively, and do not submit an address that is not theirs'). It lacks explicit routing to the sibling audit_ai_readiness as an alternative, so it falls just short of a 5.

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