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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint=false. The description adds value by explaining the tool's behavior: it probes multiple LLMs, scores visibility, and returns per-model data. It also clarifies pricing (free vs BYO) and does not contradict 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 concise (three sentences) and well-structured: it starts with core purpose, then specific details (default model, key usage), and ends with use cases. No unnecessary information.

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 has 4 parameters, no output schema, and annotations covering safety, the description provides sufficient completeness. It explains all parameter usage, return structure, and typical use cases. Could mention error handling but not essential.

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 baseline is 3. The description adds meaning beyond the schema by explaining the default model, that `_apiKey` is for Anthropic and passed directly, and that 'context' helps disambiguate. This extra context justifies a 4.

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 per model. It uses specific verbs ('probe', 'score') and resource ('LLMs', 'visibility'). It distinguishes from sibling tools like ask_pipeworx (general Q&A) and scan_competitor_ai_presence by focusing on scored visibility across multiple models.

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 use cases ('AI-marketing audits, pre-launch brand checks, competitive monitoring') and explains when to provide certain parameters (e.g., `_apiKey` for Anthropic). However, it does not explicitly contrast with sibling tools or specify when not to use this tool.

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