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

Annotations already cover read-only, idempotent, and non-destructive traits. The description adds meaningful behavioral context: the default free model, the BYO API key for Anthropic with direct cost to the user, and the returned per-model structure. It stops short of disclosing potential stochasticity of LLM scores or any rate limits, but given annotations, the added detail is sufficient.

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 the core purpose, then key configuration details, then use cases. Every sentence adds essential information with no redundancy or filler. Ideal length for a tool with this complexity.

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 there is no output schema, the description covers return values well (per-model {score, confidence, signals, raw_response} + combined view). It addresses the main operational nuance (free vs paid Anthropic calls). Minor gaps include no mention of error handling or whether the tool makes live calls each time, but for a read-only probing tool the description is adequately complete.

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?

With 100% schema coverage, the baseline is 3. The description adds value by explaining that passing `_apiKey` enables probing Anthropic and that the user pays directly, plus naming the default model (Workers AI Llama-3.3-70b). This goes beyond the schema's terse parameter descriptions to clarify cost and model selection behavior.

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 opens with a specific verb+resource: 'Probe one or more LLMs for what they know about a business / brand / product / topic and score visibility (0-100) per model.' This clearly distinguishes it from sibling tools like ask_pipeworx or deep_research by focusing on visibility scoring across LLMs. It also states the default model and output shape, removing ambiguity.

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 gives explicit use cases ('AI-marketing audits, pre-launch brand checks, competitive monitoring') and clarifies the free vs BYO-key workflow. It does not explicitly name alternatives or exclusion cases, but the use cases are clear enough to guide when to invoke this tool over 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.