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

Annotations already declare readOnly, idempotent, openWorld, and non-destructive behavior. The description adds valuable behavioral context: the free default model, the BYO-key arrangement for Anthropic (including that the user pays directly), key passthrough, and the return shape. This goes beyond the annotation baseline without contradicting it.

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: core action and score range, configuration/default model and key handling, then return shape and use cases. The most important information is front-loaded, with no 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?

Despite having no output schema, the description fully explains what the tool returns (per-model {score, confidence, signals, raw_response} plus combined view), how to configure models, cost implications, and when it is useful. The only minor gap is sibling differentiation, which was already penalized under usage guidelines. Overall, an agent can invoke this tool correctly with confidence.

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 coverage is 100%, so the baseline is 3. The description adds meaning on top of the schema: it specifies the default model, explains that _apiKey is only needed for Anthropic and is passed straight through, and notes the cost implication (BYO key, paid directly to Anthropic). This gives the agent operational context not present in the schema.

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 names a specific verb ('probe') and resource (LLMs), and clearly defines the output as a 0-100 visibility score per model. It is unambiguous and detailed, but it does not explicitly distinguish itself from close siblings such as scan_competitor_ai_presence, so it falls short of a 5.

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 provides concrete use cases ('AI-marketing audits, pre-launch brand checks, competitive monitoring'), which implies when to use the tool. However, it never contrasts the tool with alternatives like scan_competitor_ai_presence or compare_entities, leaving the agent to infer selection boundaries.

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