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

Beyond the annotations (read-only, open-world, idempotent), the description adds valuable behavioral details: the default model (Workers AI Llama-3.3-70b free), the optional Anthropic key with direct billing, and the return structure including per-model score, confidence, signals, and raw_response. This supplements the safety hints effectively, though it doesn't cover error handling or rate limits.

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 remarkably concise at three sentences, front-loading the core purpose and then layering key details (default model, billing, return format, use cases). Every sentence earns its place with zero fluff.

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 compensates by explicitly stating the return structure ('per-model {score, confidence, signals, raw_response} + a combined view'). It also covers the default/free option, optional paid Anthropic integration, and realistic use cases, making it complete for an agent to select and invoke the tool effectively.

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% for all four parameters, so the schema already documents each parameter adequately. The description adds context about the default model and _apiKey usage, but this is largely redundant with the schema's parameter descriptions. The baseline of 3 is appropriate.

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 a specific verb ('Probe one or more LLMs') and clearly defines the resource (what LLMs know about a business/brand/product/topic) and output (visibility score 0-100 per model). This distinguishes it from siblings like ask_pipeworx or compare_entities by focusing on AI visibility scoring.

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 explicit use cases ('AI-marketing audits, pre-launch brand checks, competitive monitoring'), giving clear context for when to use it. However, it does not explicitly name alternatives or state when not to use it, so it stops short of a 5.

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