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

Annotations already provide read-only, open-world, idempotent, and non-destructive hints. The description adds valuable behavioral context: default model selection, cost implications (BYO key, direct payment to Anthropic), and external API calls. It does not contradict annotations and supplies extra operational detail beyond the structured metadata.

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, each earning its place: core function and output, configuration/cost details, and use cases. It is front-loaded with the primary purpose, contains no fluff, and is efficient for an agent to parse.

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 compensates by explicitly listing the return structure (per-model score, confidence, signals, raw_response, combined view). It covers default behavior, configuration, and use cases. It stops short of addressing error handling, rate limits, or what 'signals' means, but those are secondary for this 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 is 3. The description enriches `models` and `_apiKey` by explaining the default free model and the cost/bring-your-own-key behavior, which is not fully captured in the schema. It does not add specific meaning to `context`, but the schema already handles that well.

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 a specific action ('Probe one or more LLMs') and the resource (business/brand/product/topic) with a defined output (visibility score 0-100). It distinguishes itself from sibling tools like ask_pipeworx (which likely answers questions) and scan_competitor_ai_presence (which is competitor-focused) by emphasizing generic entity 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?

It lists concrete use cases ('AI-marketing audits, pre-launch brand checks, competitive monitoring') making it easy to judge when to apply. However, it does not explicitly advise when NOT to use it or contrast with alternatives, leaving a small gap.

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