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

Annotations already indicate safe, idempotent read. Description adds valuable context: default model is free, Anthropic requires BYO key, per-model response structure includes {score, confidence, signals, raw_response}. No contradictions.

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?

Single paragraph, front-loaded with core action and result. Concise yet covers key details. Could benefit from slight structural separation (e.g., use cases vs. technical details).

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?

No output schema, but description describes return fields. Covers all parameters and their purpose. Sufficient for agent to understand usage and results. Sibling list is diverse, but tool's niche is clear.

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 100%, but description adds meaning: default model is free, _apiKey is for Anthropic and passes through directly, context helps disambiguate entity. Adds value beyond 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 uses specific verb+resource: 'Probe one or more LLMs...score visibility (0-100) per model'. It clearly distinguishes from siblings by focusing on multi-model visibility scoring for brand/entity audits.

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 states use cases: 'AI-marketing audits, pre-launch brand checks, competitive monitoring'. Provides context but does not explicitly exclude alternatives or compare to siblings like 'scan_competitor_ai_presence'.

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