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

Annotations already indicate readOnlyHint, idempotentHint, and non-destructive. The description adds critical behavioral details: default model is free, Anthropic requires BYO key and direct payment, and the return structure includes per-model scores. 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?

The description is a single paragraph that efficiently conveys the core action and key details. It is front-loaded with the main purpose. Could be slightly more structured (e.g., bullet points for return format), but it is not verbose and earns its place.

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 the tool complexity (4 parameters, no output schema), the description covers purpose, models, key, context, and return shape. It provides sufficient context for an agent to use the tool effectively, though an explicit output schema would reduce reliance on the description.

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 value beyond the schema by explaining the default model behavior for 'models' and the purpose of '_apiKey' and 'context'. This helps an agent understand parameter semantics beyond type constraints.

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 the tool's purpose: probing LLMs for knowledge about an entity and scoring visibility. It specifies the default model, the option to add Anthropic, and the return format. This distinguishes it from sibling tools like ask_pipeworx or deep_research, which are for general QA or research.

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 provides usage context: 'AI-marketing audits, pre-launch brand checks, competitive monitoring.' It also explains when to use the _apiKey parameter. However, it does not explicitly state when not to use this tool or compare it to alternatives, which would strengthen guidelines.

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