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

Annotations already declare the tool as read-only, idempotent, and non-destructive. The description adds valuable behavioral context: the default model is free, _apiKey triggers paid Anthropic calls, and the return structure includes per-model fields like score, confidence, signals, and raw_response plus a combined view. No contradictions with 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 a single, well-structured paragraph of four sentences. It front-loads the main action and returns structure, with no redundant or 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?

The description covers the tool's purpose, usage, parameters, and return structure adequately. However, it does not mention error handling, rate limits, or what happens if the entity is not found. Given the annotations and schema, the description is largely complete but could be slightly more thorough.

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 schema already documents all parameters. The description adds context about the default model and when _apiKey is needed, but this is marginal beyond the schema. Baseline 3 is appropriate.

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 action ('probe one or more LLMs'), the resource ('what they know about a business/brand/product/topic'), and the output ('score visibility 0-100 per model'). It distinguishes from sibling tools by being specifically about AI visibility checks, not generic Q&A or research.

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 on when to use the tool ('AI-marketing audits, pre-launch brand checks, competitive monitoring') and explains the default model and optional Anthropic integration. However, it does not explicitly state when NOT to use it or compare with similar tools like 'deep_research' or 'scan_competitor_ai_presence'.

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