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

Annotations already declare readOnlyHint=true and idempotentHint=true; description adds cost implications (free default vs BYO key for Anthropic), return format, and default model, providing useful context without contradiction.

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 focused sentences with front-loaded main action, no redundant words, and each sentence serves a distinct informative purpose.

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 lacking an output schema, description specifies return structure (per-model {score, confidence, signals, raw_response} + combined view) and covers use cases, parameters, and cost rules, making it fully informative for its complexity.

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, baseline 3. Description adds value by explaining _apiKey as 'BYO key — you pay Anthropic directly' and context as 'Helps disambiguate common names', surpassing schema details.

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 uses specific verbs ('Probe') and resources ('LLMs for what they know about a business/brand/product/topic and score visibility'), clearly distinguishing it from siblings like 'scan_competitor_ai_presence' by emphasizing multi-model probing with scoring.

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?

Explicitly lists use cases ('AI-marketing audits, pre-launch brand checks, competitive monitoring') and explains when to use the optional Anthropic model (requires _apiKey), but does not explicitly state when not to use the tool.

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