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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, destructiveHint false, so the safety profile is clear. The description adds valuable behavioral context: it returns per-model {score, confidence, signals, raw_response} + combined view, notes that Anthropic calls require a BYO key and direct payment, and that the default model is free. There are 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 concise: three sentences covering the core action, optional key usage, and return structure plus use cases. Every sentence adds essential information without redundancy. The most important details (probing LLMs and scoring) are front-loaded.

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?

All four parameters are adequately described, the return structure is given (per-model fields + combined view), and use cases are listed. Annotations cover behavioral traits fully. There is no output schema, but the description compensates by detailing the output. For a tool of moderate complexity with 4 parameters and no nested objects, the description is complete and leaves no major gaps.

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 already having a description. The description adds value by naming the default model ('Workers AI Llama-3.3-70b'), clarifying that _apiKey is passed straight through to Anthropic's API, and providing examples for the context parameter (e.g., 'Boston restaurant'). This enriches understanding beyond the schema alone.

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 uses specific verbs and resources: 'Probe one or more LLMs for what they know about a business / brand / product / topic and score visibility (0-100) per model.' It clearly distinguishes itself from sibling tools like ask_pipeworx (which answers questions) or scan_competitor_ai_presence (which likely does a broader scan) by focusing on visibility scoring across multiple models with a default free model.

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 states use cases: 'AI-marketing audits, pre-launch brand checks, competitive monitoring.' It also explains the default model and how to include Anthropic (BYO key). However, it does not explicitly mention when not to use it or list alternative tools for similar tasks, though the context is clear enough to guide appropriate usage.

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