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

Annotations already cover read-only, open-world, idempotent, and non-destructive traits, and the description adds important behavioral context: the default model (Workers AI Llama-3.3-70b, free), the BYO-key mechanism for Anthropic, and the cost implication ('you pay Anthropic directly'). It also discloses the return format (per-model score, confidence, signals, raw_response, combined view), which is especially valuable since no output schema is provided.

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 three sentences, front-loaded with the core purpose. Every sentence carries distinct value: the purpose and scoring, the model/cost details, and the return format plus use cases. No filler or redundancy.

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 the lack of an output schema, the description compensates by explicitly listing the return structure and per-model fields. It covers purpose, default behavior, optional components, cost implications, and use cases. All four parameters are mentioned or contextualized, making this a complete and self-sufficient description for a moderately complex 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 the baseline sits at 3, but the description adds meaningful parameter semantics beyond the schema. It clarifies that `_apiKey` is only needed if 'anthropic' is in models, notes the default model when `models` is omitted, and gives concrete examples for the `entity` parameter. This extra context helps the agent understand parameter relationships.

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 probes LLMs for knowledge about a business/brand/product/topic and scores visibility 0-100 per model. It uses specific verbs and resources, and the mention of 'AI-marketing audits, pre-launch brand checks, competitive monitoring' distinguishes it from generic ask-type sibling tools.

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 clear usage context by listing concrete use cases (AI-marketing audits, pre-launch brand checks, competitive monitoring). However, it does not explicitly mention when not to use it or name alternatives among siblings, so it stops short of full guidance.

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