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 meaningful behavioral context beyond these: the cost implication ('you pay Anthropic directly for those calls') and the BYO-key requirement. This is value-add beyond the structured annotations, though it does not cover rate limits or privacy.

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: purpose + output, default/key usage, and use cases. It is front-loaded with the primary function and avoids repetition or fluff. Every sentence contributes distinct information with no waste.

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?

Even without an output schema, the description specifies the return structure ({score, confidence, signals, raw_response} + combined view), the default model, and the optional Anthropic path. It is complete enough for selecting and invoking the tool, though it leaves the exact nature of 'signals' unexplained.

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 baseline is 3. The description adds value by clarifying the default model (Workers AI Llama-3.3-70b free), that `_apiKey` is only needed when 'anthropic' is included, and that omitting `models` yields only the free worker. These details complement the schema's own parameter descriptions.

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 opens with a specific verb ('Probe') plus the resource ('one or more LLMs') and the core output ('score visibility (0-100) per model'). It clearly differentiates from siblings like ask_pipeworx or scan_competitor_ai_presence by focusing on multi-LLM visibility scoring, and ends with concrete use cases (AI-marketing audits, pre-launch checks, competitive monitoring).

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 selection and opt-in for Anthropic via `_apiKey`. It does not explicitly name alternative tools or state when NOT to use it, but the context is sufficient to guide an agent.

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