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

Annotations already declare readOnly, openWorld, idempotent, and non-destructive. The description adds valuable behavior: it discloses that 'BYO key' means the user pays Anthropic directly and that the key is passed through to api.anthropic.com. It also describes the return shape (per-model score, confidence, signals, raw_response), which is not in 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 compact (three sentences) and front-loaded: the first sentence gives the core purpose, the second covers configuration, and the third summarizes output and use cases. Every sentence adds essential information 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?

Even though there is no output schema, the description explicitly lists the return format ('per-model {score, confidence, signals, raw_response} + a combined view'). It also explains the model selection, API key requirement, and disambiguation context. This is sufficient for a agent to invoke the tool correctly.

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?

The schema has 100% parameter coverage with descriptions. The description adds extra meaning: it states the default model (Workers AI Llama-3.3-70b) is free, clarifies the _apiKey is only needed when Anthropic is in models, and gives examples for the entity parameter. This goes beyond the schema's own descriptions.

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's function: probing LLMs for knowledge about an entity and scoring visibility. It uses a specific verb ('Probe') and resource ('LLMs'), and the outcome (0-100 visibility score) is explicit. It also distinguishes itself from broader research tools by focusing on AI visibility scoring.

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 concrete use-case context ('AI-marketing audits, pre-launch brand checks, competitive monitoring') and explains when to use the optional Anthropic key. However, it does not explicitly mention when not to use it or contrast with sibling tools like scan_competitor_ai_presence, so it lacks explicit exclusion guidance.

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