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

With no annotations provided, the description carries full burden. It discloses the process (fixed prompt set, N runs, non-determinism measured), output characteristics (0-100 score, 95% confidence interval, published weights, citation lists), and guarantees (Ed25519-signed, never fabricated). This is rich, honest behavioral disclosure beyond what any annotation might provide.

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 a single dense paragraph of roughly 80 words, front-loaded with the core purpose. It packs substantial detail (method, outputs, comparisons, pricing) without extraneous fluff, though the all-caps emphasis and parenthetical tail slightly reduce readability. Overall, every clause earns its place.

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?

No output schema exists, so the description must explain return values. It enumerates all key outputs: AEO score, confidence interval, weights, presence rate, share-of-voice, citation rate, sentiment, and cited sources. It also covers input semantics contextually, pricing, and the non-fabrication guarantee. The tool is complex enough that this level of detail is necessary and fully provided.

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 conceptual context (e.g., 'buyer-intent prompt set', 'position-weighted share-of-voice') but does not provide parameter-specific guidance beyond what the schema already offers. It neither compensates nor penalizes, staying at the standard level.

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 it computes an answer-engine visibility score (AEO Score) for a brand/product/agent, with specific details on methodology and outputs. It distinguishes itself from external tools (Profound/Semrush-AI) but does not explicitly differentiate from sibling tools like onyx_ai_visibility or onyx_market_rank, so it misses the full distinction criterion.

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 implies when to use the tool: when you need a signed, auditable AEO measurement with disclosed weights and confidence intervals. It explicitly contrasts with Profound/Semrush-AI (hidden weights, single daily run), giving clear context for choosing this tool. However, it does not state hard exclusions or when an alternative sibling tool would be more appropriate.

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