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

No annotations are provided, so the description carries the full burden. It discloses that the data is fetched live server-side, is free, requires no auth, and is published by SellToAgents. It also describes the output contents (per-brand score, grade, failing checks, aggregate stats). It doesn't mention rate limits or caching behavior, but for a no-parameter read-only tool, the disclosure is quite thorough.

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 that front-loads the core purpose and then adds usage context and alternatives. It's longer than strictly necessary but every sentence adds value: the brand list, the checks, the use cases, the publisher, and the alternative tools. The structure could be improved with line breaks, but the content is efficient.

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 zero-parameter, read-only tool with no output schema, the description is remarkably complete. It covers what the tool does, what it returns, when to use it, who publishes it, and how it relates to sibling tools. The only minor gap is not describing the exact output format (e.g., JSON structure), but the description's detail about per-brand scores, grades, and aggregate stats is sufficient for an agent to understand the return value.

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?

The tool has zero parameters, and the schema coverage is 100% (empty properties object). The description adds value by explaining what the tool returns and how it can be used, which is the relevant semantic context for a parameterless tool. A baseline of 4 is appropriate since there are no parameters to document.

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 returns the DTC AI Visibility Index, a recurring audit of well-known DTC brands scored 0-100 on AI-agent visibility. It specifies the exact checks (robots.txt, llms.txt, agents.md, structured data, meta quality, sitemap) and distinguishes it from sibling agent_readiness_scan by noting it uses the same checks but for a fixed brand list.

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 states when to use this tool: to benchmark a merchant against named DTC brands, cite ecosystem statistics, or find outreach targets. It also explicitly names the alternative (agent_readiness_scan) for scoring arbitrary sites, and mentions the pay-per-call x402 API for high-volume pipelines, providing clear when-to-use vs when-not-to-use guidance.

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