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 valuable behavioral details beyond these: the default model (Workers AI Llama-3.3-70b), the free vs. paid billing model for Anthropic calls, and the fact that the API key is passed straight through. This gives agents insight into cost and side-effect implications.

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, front-loaded with the core action, followed by model options and use cases. Every sentence adds distinct value—purpose, configuration/billing, and output/use cases—with no fluff or repetition.

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 explicitly states the return structure ('per-model {score, confidence, signals, raw_response} + a combined view'). It covers parameter behavior (default model, optional Anthropic key), use cases, and sample entity formats in the schema. For a read-only probe tool, this is complete and leaves no major gaps for an agent to infer.

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 parameters are already well-documented. The description adds context about the default model and the condition for using _apiKey, but this largely mirrors schema text. Thus the description provides only marginal additional meaning beyond the schema, meriting the baseline score of 3.

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 'Probe one or more LLMs for what they know about a business / brand / product / topic and score visibility (0-100) per model,' giving a specific verb ('probe'), resource ('LLMs'), and output (visibility score). This clearly distinguishes it from sibling tools like ask_pipeworx (general Q&A) and scan_competitor_ai_presence (competitor-focused analysis).

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 states concrete use cases ('AI-marketing audits, pre-launch brand checks, competitive monitoring') and clarifies when to pass '_apiKey' for Anthropic (BYO key). It does not explicitly name alternatives or exclusions, but the context is clear enough for an agent to select this tool over siblings.

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