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

With annotations already declaring readOnly/openWorld/idempotent, the description adds valuable context: the default model and cost implication ('you pay Anthropic directly'), the return shape (per-model {score, confidence, signals, raw_response} + combined view), and that _apiKey passes straight through. This goes beyond what annotations convey.

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, front-loading the core function, then covering configuration/cost, return format, and use cases. No filler or redundant restatement of the tool name; every sentence contributes new information.

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?

For a read-only, 4-parameter probe tool with no output schema, the description covers the essentials: purpose, default behavior, optional customization, response shape, and typical use cases. Minor gaps like error handling or what happens if the passed key is invalid are not addressed, but the overall picture is solid.

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%, but the description adds semantic meaning beyond the schema: it clarifies that omitting 'models' defaults to the free workers-ai model, explains the role of _apiKey as a BYO Anthropic key, and implicitly describes how parameters interact. This elevates it above the 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 a clear resource ('one or more LLMs'), then explains the output (visibility score 0-100 per model). It also differentiates from siblings by focusing on any entity's AI visibility, unlike tools like ask_pipeworx or scan_competitor_ai_presence, and lists concrete use cases.

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 explicit use contexts ('AI-marketing audits, pre-launch brand checks, competitive monitoring') and explains when to extend with _apiKey. However, it does not mention any alternatives or when not to use this tool, so it stops short of full exclusionary guidance.

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