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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and no destructive actions. The description adds valuable behavioral details: it executes across models, returns per-model scores with confidence and raw responses, defaults to a free model, and passes `_apiKey` directly to Anthropic without processing. 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?

Description is a single paragraph but well-structured: it opens with the core action and return value, then covers model options and use cases. Every sentence adds necessary information without redundancy. Front-loaded with the primary function.

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?

Despite no output schema, the description specifies the return shape (per-model score, confidence, signals, raw_response + combined view). It covers all parameters and use cases. However, it could mention potential timeout issues or API key validation, which would improve completeness. Still, very good for a tool with simple inputs and no output schema.

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 for all 4 parameters. The description enhances understanding by providing examples for `entity` (e.g., 'Pipeworx'), clarifying default behavior for `models`, explaining the payment implication for `_apiKey`, and specifying that `context` disambiguates common names. This adds value beyond the schema.

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?

Description clearly states the tool's purpose: probing LLMs for knowledge about a business/brand/product/topic and scoring visibility (0-100). It distinguishes from sibling tools by focusing on visibility score rather than question answering or research. The verb 'probe' and resource 'LLMs' make the action specific.

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?

Description explicitly lists use cases: AI-marketing audits, pre-launch brand checks, competitive monitoring. It explains when to use the optional `_apiKey` parameter. While it doesn't explicitly contrast with sibling tools like `compare_entities` or `scan_competitor_ai_presence`, the stated use cases provide clear guidance on when this tool is appropriate.

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