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

Annotations already indicate safe, idempotent, read-only behavior. The description adds value by disclosing the return structure (per-model scores, confidence, signals, raw_response + combined view) and the cost implication of using Anthropic (BYO key). No contradictions with 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 extremely concise (4 sentences) yet packs all essential information: purpose, scoring range, default model, optional Anthropic, return structure, and use cases. Every sentence earns its place with no 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?

For a tool with no output schema, the description adequately explains the return format (per-model and combined). It covers key behavioral aspects (default free, optional cost, disambiguation via context). Given the tool's complexity and that only one parameter is required, the description is fully complete for an agent to invoke 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?

Schema has 100% description coverage, meeting the baseline of 3. The description adds meaningful context beyond schema: explains default model behavior, clarifies the API key's purpose (only needed for Anthropic), and gives usage examples for the context parameter ('Boston restaurant').

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 visibility of a business/brand/product and scoring it 0-100. It specifies the default model and optional Anthropic, and distinguishes from siblings by focusing on AI visibility audits across multiple models, which is a unique capability among the listed sibling tools.

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 explicit use cases: AI-marketing audits, pre-launch brand checks, competitive monitoring. It explains default behavior (free Workers AI) and when to provide an API key for Anthropic. It doesn't explicitly exclude alternatives but gives enough context for appropriate selection.

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