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

Annotations already declare readOnlyHint: true and openWorldHint: true. The description adds meaningful behavioral details: default model is free, passing `_apiKey` incurs direct Anthropic costs, and the return structure (per-model {score, confidence, signals, raw_response} + combined view). This enriches the annotation profile without contradiction.

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?

Three sentences, each earning its place: first states the core function, second covers default model and key handling, third explains return format and use cases. Front-loaded with the primary purpose, no filler.

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 appropriately discloses return shape ('per-model {score, confidence, signals, raw_response} + combined view'). It covers parameters, defaults, costs, and use cases. Slightly more detail on what 'signals' means would raise it to 5, but as-is it is adequate for a 4-parameter 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 description coverage is 100%, so baseline is 3. The description adds value by clarifying the default for 'models' (Workers AI Llama-3.3-70b, free) and the cost implication of '_apiKey' ('BYO key — you pay Anthropic directly'), which goes beyond the schema's 'optional' label.

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 states a specific action ('Probe one or more LLMs') and resource ('business / brand / product / topic') with a clear output ('score visibility (0-100) per model'). It distinguishes itself from siblings like 'scan_competitor_ai_presence' by focusing on LLM knowledge scoring and explicitly listing use cases (AI-marketing audits, pre-launch brand checks).

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 usage context with 'Useful for AI-marketing audits, pre-launch brand checks, competitive monitoring.' It does not explicitly name alternatives or exclusions, but the use cases imply when to reach for this tool over siblings.

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