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

Beyond annotations (readOnly, idempotent), the description adds that the tool probes multiple LLMs, returns per-model score/confidence/signals/raw_response, and mentions the free default model vs. paid Anthropic. 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 two sentences plus a use-case list, all front-loaded with the core action and results. No redundant or extraneous information; every sentence adds value.

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?

With 4 parameters, no output schema, moderate complexity, the description covers what the tool does, how to use it, and what it returns (per-model fields and combined view). Minor omission: doesn't detail 'signals' or 'raw_response' structure, but overall sufficient for an agent to invoke 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 descriptions exist for all 4 parameters, so baseline is 3. The description adds context like default model and purpose of _apiKey, but this is minimal extra value given the schema already explains each parameter adequately.

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 verb 'probe' and the resource 'LLMs' for knowledge about an entity, scoring visibility per model. It distinguishes from siblings by specifying this is an AI visibility audit tool, contrasting with e.g., 'scan_competitor_ai_presence' which may have a different focus.

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 cases: 'AI-marketing audits, pre-launch brand checks, competitive monitoring.' It also explains when to use the optional API key. However, it does not explicitly exclude scenarios or compare directly to alternate tools, limiting guidance on when not to use.

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