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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and non-destructive behavior. The description adds valuable context beyond annotations: calling Anthropic incurs direct costs (BYO key), the default is free Workers AI, and the return shape (per-model {score, confidence, signals, raw_response} + combined view). This extra detail about external calls and cost is useful.

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: first sentence states the core purpose and output, second provides model/cost details, third lists use cases. Every sentence adds distinct value with no redundancy, and the most important information is 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?

Given the tool's moderate complexity (4 params, 1 required, no enums) and rich annotations, the description covers the essential aspects: purpose, default behavior, cost implications, return values, and use cases. While there is no output schema, the description explicitly lists the return structure, which compensates. Minor gaps like pagination or error handling are not critical for this read-only 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%, so parameters are already documented. The description adds meaning beyond the schema by explaining the free default model (workers-ai), that `_apiKey` enables Anthropic probing with direct payment, and that `context` helps disambiguate. This clarifies defaults and the purpose of `_apiKey` beyond its schema description.

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 ('one or more LLMs for what they know about a business/brand/product/topic') and clearly distinguishes from siblings by focusing on visibility scoring (0-100) per model. It also differentiates from similar tools like 'scan_competitor_ai_presence' by emphasizing multi-model probing and quantitative 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 explicitly names use cases: 'AI-marketing audits, pre-launch brand checks, competitive monitoring', providing clear context for when to use. It also explains the default model and optional Anthropic probing, giving practical guidance on invocation, but it does not explicitly mention when not to use it or name alternative tools.

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