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

The description adds valuable behavioral details beyond the annotations: the default model (Workers AI Llama-3.3-70b) is free, passing '_apiKey' enables Anthropic with direct billing to the user, and the return format is per-model {score, confidence, signals, raw_response} plus a combined view. The annotations already declare read-only/idempotent, so these cost and output details enhance transparency without redundancy.

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 with no redundancy. It front-loads the core action, then packs in default behavior, cost notes, return structure, and use cases efficiently. Every sentence earns its place.

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's moderate complexity and lack of an output schema, the description adequately covers the return structure, use cases, and billing nuances. Misses explicit error-handling or limitation notes, but for a read-only visibility probe, the description is sufficient for an agent to select and invoke the tool correctly.

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. However, the description adds meaning beyond the schema by clarifying the '_apiKey' parameter's cost implication ('you pay Anthropic directly') and explaining the default model behavior, which enriches the semantic understanding of the 'models' parameter.

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 verb ('Probe') and target resource ('LLMs'), and clearly defines the output as a visibility score (0-100) per model. This distinguishes it from sibling tools focused on search, research, or comparison, and scopes the subject to business/brand/product/topic.

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 lists concrete use cases: 'AI-marketing audits, pre-launch brand checks, competitive monitoring.' This gives clear context for when to use the tool, but it does not explicitly name alternative tools or state when not to use it, so it falls short of a full exclusionary guideline.

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