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

Annotations already provide readOnlyHint, idempotentHint, etc. The description adds that the tool returns per-model {score, confidence, signals, raw_response} + combined view, and that _apiKey is passed to Anthropic. It does not mention rate limits or cost, but adds meaningful context beyond 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?

The description is a single paragraph that efficiently conveys purpose, usage, return format, and parameter nuances. It front-loads the main action. A more structured format (e.g., bullet points for returns) would improve readability, but current version is not verbose.

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 4 parameters and no output schema, the description covers return format sufficiently, explains the apiKey flow, and specifies default model. Minor omissions like error handling or timeout behavior exist, but the essential information for correct invocation 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 coverage is 100% with parameter descriptions. The description supplements by explaining the default model, the role of _apiKey, and gives examples for entity and context. This adds value beyond the schema, raising it above the baseline of 3.

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 clearly states the tool probes LLMs for knowledge about a business/brand/product/topic and returns visibility scores. It uses specific verbs like 'probe' and 'score', and names the resource. Though there is a sibling tool 'scan_competitor_ai_presence' that could overlap, the description does not explicitly differentiate, preventing 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 mentions use cases: AI-marketing audits, pre-launch brand checks, competitive monitoring. It also explains when to provide _apiKey (for Anthropic) and defaults to Workers AI. However, it does not state when to avoid this tool or suggest alternatives like scan_competitor_ai_presence.

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