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

Annotations already indicate read-only, open-world, idempotent, and non-destructive behavior. The description adds valuable behavioral context: cost implications (free default, BYO key for Anthropic), default model details, and expected return structure (score, confidence, signals, raw_response). 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 two sentences: the first covers core functionality and output format, the second adds model/pricing details. Every word serves a purpose, and it is front-loaded with the primary action. No redundancy or fluff.

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 explicitly lists the per-model return fields (score, confidence, signals, raw_response) and a combined view. Combined with annotations covering safety and idempotency, the description is complete for an agent to understand how to invoke and interpret results.

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?

All four parameters are described in the schema (100% coverage). The description enhances understanding by explaining the default model for 'models', the purpose of '_apiKey' (BYO key, direct billing), and disambiguation role of 'context'. This adds actionable guidance 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?

The description clearly states the tool's purpose: probing LLMs to measure visibility (0-100) for entities. It specifies the verb 'probe', the resource 'LLMs', and the business use cases (AI-marketing audits, pre-launch brand checks, competitive monitoring). This differentiates it from sibling tools like 'scan_competitor_ai_presence' which might have a narrower focus.

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 context on when to use (AI-marketing audits, brand checks) and how to optionally include Anthropic with a key. However, it does not explicitly state when not to use or list alternatives among the 40+ sibling tools, leaving room for improvement.

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