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

Annotations already indicate readOnly and idempotent behavior, and the description adds valuable context: it explains that the tool makes external LLM calls, that Anthropic calls require a user-provided API key, and that the user pays Anthropic directly. It also discloses the return structure (per-model {score, confidence, signals, raw_response} + combined view), which is beyond the annotation hints. It does not mention rate limits or potential non-determinism, but the confidence field partially covers uncertainty.

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 four short sentences, each covering a distinct aspect: purpose, configuration/cost, return format, and use cases. There is zero filler or redundancy. It is front-loaded with the core purpose, making it easy to scan, and every sentence 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?

With no output schema, the description takes responsibility for explaining what the tool returns, and it does so clearly: 'Returns per-model {score, confidence, signals, raw_response} + a combined view.' It also covers model selection, cost implications, and typical use cases. Given the moderate complexity (4 params, 1 required), this is a complete description that leaves no major questions unanswered.

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 description coverage is 100%, so the baseline is 3. The description adds meaningful parameter context by stating the default model (Workers AI Llama-3.3-70b, free) and explaining that _apiKey is only needed for Anthropic and is passed straight through. It also clarifies the `context` parameter's disambiguation purpose implicitly through the examples. This goes beyond the schema descriptions, making the tool easier to invoke correctly.

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 and resource: 'Probe one or more LLMs for what they know about a business / brand / product / topic and score visibility.' It clearly states the output (0-100 visibility score) and the scope (per model), distinguishing it from sibling tools like ask_pipeworx or deep_research. The use cases (AI-marketing audits, pre-launch brand checks) further clarify its niche.

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 context for when to use the tool: 'Useful for AI-marketing audits, pre-launch brand checks, competitive monitoring.' It also explains how to choose between models (default Workers AI vs. Anthropic with _apiKey), which acts as practical usage guidance. However, it does not explicitly name alternative tools or state when not to use this tool, so it does not earn a 5.

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