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

Annotations already mark the tool as readOnly, openWorld, idempotent, and non-destructive. The description adds critical behavioral details beyond annotations: it returns per-model {score, confidence, signals, raw_response} plus a combined view, and explicitly states that probing Anthropic requires a BYO key and that the user pays Anthropic directly. This cost and key-handling information is valuable for the agent.

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, well-structured paragraph with two sentences. The first sentence front-loads the core action and output. Every sentence adds value: purpose, default behavior, optional key handling, return format, and use cases. No wasted words.

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 tool has 4 parameters (1 required), no output schema, and annotations covering safety, the description is complete. It explains the return format, default model, optional Anthropic integration with cost implications, and multiple use cases. The agent can understand the tool's full capability and constraints without ambiguity.

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 adds concrete examples for each parameter (e.g., 'Pipeworx' for entity, 'workers-ai' for models, 'sk-ant-...' for _apiKey, 'Boston restaurant' for context), which helps the agent form correct invocations. This extra detail raises the score to 4.

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 an entity and returns a visibility score per model. It specifies the verb 'probe', the resource 'one or more LLMs', and the output format. The description also distinguishes from sibling tools by mentioning specific use cases like 'AI-marketing audits, pre-launch brand checks, competitive monitoring', which are not covered by siblings such as 'scan_competitor_ai_presence'.

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 clear contexts for use: AI-marketing audits, pre-launch brand checks, competitive monitoring. It also explains optional probing with Anthropic via BYO key. However, it does not explicitly state when not to use the tool or suggest alternatives among siblings, missing an opportunity for full guidance.

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