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

Annotations already declare readOnlyHint=true and idempotentHint=true, but the description adds critical behavioral context: invoking Anthropic requires a user-provided _apiKey and incurs direct cost ('BYO key — you pay Anthropic directly'). It also clarifies that the default Workers AI model is free, which is not apparent from annotations.

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, each with a distinct purpose: the action and output, the model selection and cost behavior, and the use cases. It is front-loaded with the core function and contains no redundant filler relative to the schema.

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?

Since there is no output schema, the description compensates by explicitly listing the return shape ('per-model {score, confidence, signals, raw_response} + a combined view'). It also covers model selection, key handling, and use cases, making it fully sufficient for an AI agent to decide when and how to invoke the tool.

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 coverage is 100% with good descriptions for all parameters. The description goes further by explaining the default model ('Workers AI Llama-3.3-70b'), the relationship between _apiKey and models ('pass _apiKey to also probe Anthropic'), and how omitting 'models' defaults to workers-ai.

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') and resource ('LLMs for what they know about a business/brand/product/topic') and clearly states the output ('score visibility (0-100) per model'). It distinguishes itself from siblings like 'scan_competitor_ai_presence' by focusing on multi-LLM visibility scoring with a numerical output.

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'), giving clear context for when to use it. However, it does not name alternative tools or exclusions, such as noting that 'scan_competitor_ai_presence' might be more suitable for competitor-focused scans.

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