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

Annotations already establish readOnly, openWorld, idempotent, and non-destructive behavior. The description goes beyond by disclosing that Workers AI Llama-3.3-70b is the free default, that using Anthropic requires a BYO key and direct payment, and that the return includes per-model score/confidence/signals/raw_response plus a combined view. This adds meaningful operational and cost context with no contradictions.

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?

Four sentences, all information-dense: purpose, default model/cost, return format, and use cases. No filler or repetition. Front-loaded with the primary action and output.

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 moderate complexity (4 params, one required) and no output schema, but the description provides the return structure clearly and covers key operational details (model selection, API key, disambiguation via context). The tool is fully understandable without needing additional documentation.

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%, and the schema already explains each parameter (entity, models, _apiKey, context). The description adds marginal value by noting that '_apiKey' is passed straight through to api.anthropic.com and that models are optional, but it does not substantially enrich parameter understanding beyond the schema baseline.

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 a specific verb ('Probe') and clearly defines the resource (LLMs), the action (score visibility 0-100), and the scope (business/brand/product/topic). It also adds model default and optional Anthropic, which distinguishes it from siblings like ask_pipeworx (direct Q&A) and scan_competitor_ai_presence (competitor-focused).

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.' This provides clear context for when to use the tool, though it does not explicitly name alternative tools or exclusion criteria. Still, the guidance is enough to help an agent decide.

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