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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the safety profile is clear. The description adds value by specifying the default model is free and the cost implications of using Anthropic (BYO key, direct payment). It also reveals the return structure: per-model {score, confidence, signals, raw_response} + combined view. No contradictions with 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 concise, consisting of three sentences that front-load the purpose. Every sentence adds unique value: first sentence explains what it does and default behavior, second sentence covers optional Anthropic integration and cost, third sentence lists use cases and return format. No wasted 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?

Given no output schema, the description compensates by listing return fields (score, confidence, signals, raw_response, combined view). The input schema is fully described with 100% coverage. The tool's complexity is moderate with 4 parameters (1 required), and the description addresses all key aspects: default model, optional models, API key usage, disambiguation context, and practical applications.

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%, so the baseline is 3. The description adds significant meaning: explains that '_apiKey' is only needed if 'anthropic' is in models and that it is passed straight through to api.anthropic.com. It also clarifies that 'context' helps disambiguate common names. These details go beyond the schema descriptions.

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 'probes LLMs for what they know about a business/brand/product/topic and scores visibility (0-100) per model'. It uses specific verbs (probe, score) and resources (LLMs, entity). It distinguishes from siblings by focusing on multi-model visibility scoring, unlike sibling tools like 'ask_pipeworx' which are single-query tools.

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'. It explains default model (Workers AI Llama-3.3-70b free) and when to provide an API key for Anthropic. However, it does not explicitly state when not to use or compare to similar siblings like 'compare_entities' or 'scan_competitor_ai_presence'.

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