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

Annotations (readOnlyHint, openWorldHint, idempotentHint, destructiveHint false) already indicate safe, idempotent read behavior. The description adds valuable context: default model is Workers AI (free), Anthropic requires a BYO key passed to api.anthropic.com, and returns per-model details. It does not mention rate limits or potential cost implications beyond the API key, but overall adds significant behavioral context beyond 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 four sentences, front-loaded with the primary purpose and key details. Every sentence adds essential information: purpose, default model, optional Anthropic with cost note, and output structure. No filler or redundancy.

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?

The tool has no output schema, so the description compensates by stating: 'Returns per-model {score, confidence, signals, raw_response} + a combined view.' This is sufficient for understanding the output. For a simple 4-parameter probing tool, the description covers the necessary aspects. Minor improvement could detail what 'signals' or 'combined view' contain, but not critical.

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 clear descriptions for each parameter. The description adds value by: giving examples for entity (e.g., 'Pipeworx', 'OpenInvoice'), clarifying that models defaults to workers-ai, explaining that _apiKey is only needed if 'anthropic' is in models, and that context helps disambiguate. This goes beyond the schema, though the schema already does a good job.

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 clearly states the tool's purpose: 'Probe one or more LLMs for what they know about a business / brand / product / topic and score visibility (0-100) per model.' It specifies the verb (probe, score), resource (LLMs), and output format. This distinguishes it from sibling tools like 'ask_pipeworx' (general Q&A) and 'scan_competitor_ai_presence' (different scope).

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 clear usage context: 'Useful for AI-marketing audits, pre-launch brand checks, competitive monitoring.' It explains when to use the optional _apiKey for Anthropic. However, it does not explicitly mention when NOT to use this tool or list alternatives among siblings, which would improve score to 5.

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