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

Annotations already provide readOnlyHint, openWorldHint, idempotentHint, destructiveHint (false). The description adds behavioral context: it probes LLMs, scores visibility, returns per-model results, and mentions cost implications for Anthropic (BYO key). No contradiction 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?

Three sentences, front-loaded with main purpose. Every sentence adds unique value: first describes action, second adds technical/cost detail, third lists use cases. No 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 outlines return structure: per-model {score, confidence, signals, raw_response} + combined view. With 4 parameters and clear use cases, the description is complete and sufficient.

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 descriptions for all 4 parameters. The description adds context beyond schema: default model, cost note for Anthropic, and disambiguation hint for entity. This adds meaningful value while schema already covers basics.

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 it probes LLMs for knowledge about an entity and scores visibility (0-100) per model, with specific verb 'probe' and resource 'LLMs'. It distinguishes from siblings like 'scan_competitor_ai_presence' by focusing on individual model visibility rather than competitive 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 states use cases: 'AI-marketing audits, pre-launch brand checks, competitive monitoring.' It also explains when to provide _apiKey (for Anthropic). However, it does not explicitly exclude alternative tools or state when not to use, leaving some ambiguity.

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