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 valuable behavioral context beyond this: it discloses that probing Anthropic requires a BYO key and that 'you pay Anthropic directly for those calls,' implying external network calls with cost implications. It also details the default model (Workers AI Llama-3.3-70b free) and the return structure, providing transparency about side effects and behavior.

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 three sentences long, front-loaded with the core purpose, followed by key configuration details and return format. Every sentence provides necessary information without fluff or redundancy.

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 tool has no output schema, so the description compensates by explicitly listing the return shape: 'per-model {score, confidence, signals, raw_response} + a combined view.' It also explains the default behavior and cost implications of using Anthropic. It is nearly complete, but could add a note about potential rate limits or error handling, though these are not critical for a read-only probe.

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 each parameter already having a descriptive comment (e.g., `_apiKey` explains it is passed straight through to api.anthropic.com). The description adds little beyond the schema, mainly clarifying the default model. Baseline 3 is appropriate as the schema carries the parameter semantics.

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 purpose: 'Probe one or more LLMs for what they know about a business / brand / product / topic and score visibility (0-100) per model.' It specifies the verb (probe), resource (LLMs), and output (visibility score). This distinguishes it from sibling tools like scan_competitor_ai_presence by focusing on cross-model visibility scoring rather than competitor-specific scanning.

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 use cases: 'AI-marketing audits, pre-launch brand checks, competitive monitoring.' This gives context on when to use the tool. However, it does not explicitly name alternatives or state when not to use it, so it falls short of a 5 but is above a 3.

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