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

Annotations already indicate readOnlyHint, openWorldHint, idempotentHint, destructiveHint=false, so the description doesn't need to repeat safety. It adds valuable context: returns per-model objects with score, confidence, signals, raw_response, and a combined view. It also discloses cost implication for Anthropic probes. 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 concise (two sentences) with no wasted words. It front-loads the primary action and return value. The structure is clear and easy 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?

Given that there is no output schema, the description adequately describes the return structure (per-model and combined view). It covers all key aspects: purpose, parameters, default vs. optional costs, and return format. The tool is not overly complex, and the description is fully sufficient.

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 baseline is 3. The description adds significant meaning beyond the schema: it specifies the default model (Workers AI Llama-3.3-70b, free), explains when `_apiKey` is needed (only for Anthropic), clarifies that models array can be omitted to use only the default, and gives an example for entity. This greatly aids the agent in parameter usage.

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 a specified entity and returning visibility scores per model. It uses specific verbs ('probe', 'score') and specifies the resource (LLMs). Although there is a sibling tool 'scan_competitor_ai_presence', the focus on scoring visibility (0-100) across multiple models distinguishes it adequately.

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 usage context: 'AI-marketing audits, pre-launch brand checks, competitive monitoring.' It explains when to use it and mentions default model vs. optional Anthropic (with BYO key). However, it does not explicitly state when not to use it or compare to siblings, so it's slightly below a perfect score.

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