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

Annotations already declare readOnly, openWorld, idempotent, and non-destructive traits. The description adds valuable context beyond annotations: the default model (Workers AI Llama-3.3-70b) is free, passing _apiKey opts into paid Anthropic calls ('you pay Anthropic directly'), and it discloses the exact return shape (per-model {score, confidence, signals, raw_response} + combined view). No contradiction found.

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, each carrying essential information: purpose, operational details (default model, cost, return fields), and use cases. The description is front-loaded with the primary purpose and contains zero filler words.

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?

Despite having no output schema, the description enumerates the return fields (score, confidence, signals, raw_response, combined view) and explains how to enable Anthropic. Combined with the 100% schema coverage, an agent has all necessary information to select and invoke the tool correctly.

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% (all 4 parameters have descriptions), so the baseline is 3. The description reinforces that _apiKey is for enabling Anthropic and that the default model is workers-ai, but it does not add new parameter behavior beyond what the schema already documents. The added nuance about the free default is minor.

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') targeting LLMs' knowledge of a business/brand/product/topic, and clearly defines the output (a 0-100 visibility score per model). This distinguishes it from sibling tools like ask_pipeworx or deep_research by focusing on cross-model visibility scoring rather than answering questions or research.

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?

Explicitly lists concrete use cases: 'AI-marketing audits, pre-launch brand checks, competitive monitoring.' This gives clear context for when to invoke the tool. It does not name alternative tools or explicitly say when not to use it, but the stated use cases are sufficient guidance for an agent.

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