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

Annotations already provide readOnlyHint=true and openWorldHint=true, so the description doesn't need to re-assert safety. The description adds valuable behavioral context: the default model is free (Workers AI Llama-3.3-70b), passing `_apiKey` incurs direct Anthropic costs, and it discloses that the key is forwarded to api.anthropic.com. This goes beyond the annotations and helps the agent understand external dependencies and cost implications.

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 compact and front-loaded: the first sentence captures the core purpose and output, while the second sentence adds operational details without waste. Every clause serves a purpose: default model, cost note, return format, and use-case enumeration. It avoids fluff and is well organized.

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 lacking an output schema, the description explicitly lists the per-model return structure ('{score, confidence, signals, raw_response}') and mentions a combined view. It covers the main behavioral aspects (read-only, external calls, cost), and the annotations cover safety. Given the moderate complexity and the presence of sibling tools, the description is sufficiently complete for an agent to select and invoke the tool correctly.

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 context beyond the schema: it explains which model is used by default ('Default model is Workers AI Llama-3.3-70b (free)'), clarifies that the `_apiKey` is passed through to Anthropic ('BYO key — you pay Anthropic directly'), and gives examples for the `entity` field. This enriches the schema's documentation without redundancy.

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: 'Probe one or more LLMs for what they know about a business / brand / product / topic and score visibility (0-100) per model.' It uses a specific verb ('probe'), names the resource (LLMs), and specifies the output (visibility scores). It also distinguishes itself from sibling tools like ask_pipeworx (conversational queries) and scan_competitor_ai_presence by focusing on multi-model AI visibility scoring.

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 concrete use cases: 'AI-marketing audits, pre-launch brand checks, competitive monitoring.' It also explains when to pass `_apiKey` (to probe Anthropic) and clarifies the default model behavior. However, it does not explicitly state when not to use this tool or name alternative tools for related tasks, so it's not a full 5.

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