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

Annotations already declare readOnly/Open World/idempotent/non-destructive, and the description supplements this with key behavioral details: default model and cost implications, per-model return structure (score, confidence, signals, raw_response), and that _apiKey is passed through to Anthropic directly. 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?

Four densely informative sentences with zero filler. Every sentence contributes unique value: what it does, default model and pricing, return format, and use cases. Properly front-loaded with the core purpose first.

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?

Given 4 parameters (1 required), no output schema, and moderate complexity, the description fully covers return shape, model options, key handling, and intended scenarios. There are no obvious gaps in what an agent needs to decide whether and how to invoke this tool.

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%, so baseline is 3. The description adds meaningful context beyond the schema: explains that _apiKey is only needed for anthropic model and that you pay Anthropic directly, that context helps disambiguate common names, and that models can be omitted for default behavior. This elevates it above baseline.

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 states a specific action ('Probe one or more LLMs') with a clear resource (business/brand/product/topic) and measurable outcome (visibility score 0-100 per model). It clearly distinguishes from siblings like scan_competitor_ai_presence by focusing on LLM knowledge scoring rather than marketing presence 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?

Provides clear context on when to use (AI-marketing audits, pre-launch brand checks, competitive monitoring) and explains model selection (default free Workers AI vs BYO Anthropic key). It doesn't explicitly name alternative tools to use instead, but the use-case guidance is sufficient for most agents.

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