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

Annotations already declare readOnlyHint=true, openWorldHint=true, idempotentHint=true, and destructiveHint=false. The description adds value by detailing that it probes LLMs, returns per-model data (score, confidence, signals, raw_response) plus a combined view, and notes that Anthropic calls require a BYO API key and incur direct costs. No contradictions.

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 about 4 sentences. It leads with the core action and outcome, follows with key details (default model, Anthropic key), then return format, and ends with use cases. Every sentence serves a purpose with no redundancy or wasted words.

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?

For a tool with 4 parameters, rich annotations, and no output schema, the description covers the essential aspects: what it does, key parameters, return structure, and use cases. It does not mention error handling, rate limits, or specific output format details, but these gaps are minor given the annotations and schema coverage.

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%, with each parameter well-described. The description adds context beyond the schema: it specifies the default model (Workers AI Llama-3.3-70b free) and clarifies that the `_apiKey` is passed through to Anthropic. This helps the agent understand parameter behavior without relying solely on schema names.

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 verb 'probe', the resource 'LLMs', and the outcome 'score visibility (0-100) per model'. It distinguishes this tool from siblings like 'ask_pipeworx' and 'scan_competitor_ai_presence' by focusing on AI visibility auditing rather than general questions or competitor scanning.

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 explicitly lists use cases: 'AI-marketing audits, pre-launch brand checks, competitive monitoring'. It also explains the default model and the optional Anthropic key with cost implications. While it doesn't specify when not to use or name alternative tools, the context is clear and sufficient for an agent.

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