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

With no annotations, the description carries the burden and does real work: it discloses the paid model ('$0.003 USDC per call via x402'), a critical behavioral fact an agent must know before invoking, plus the aggregate nature of the call (all checks in one request). It omits latency, rate limits, error/refund behavior for unregistered or invalid domains, and the truncated example cutting off at 'https':{'reac' fails to show whether reachability/status codes are returned.

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?

Front-loaded with capabilities and use cases, then pricing, then example – a sensible order. The embedded example JSON is a reasonable payload but is visibly truncated mid-string ('https':{'reac'), which costs a bit of clarity and wastes characters on an incomplete object.

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 single-parameter, moderate-complexity tool with no output schema, the description largely supplies the missing return-value context via the sample payload and states the cost. The truncation of that sample, plus no word on unregistered-domain or failure responses, leaves minor gaps that an agent might need to probe.

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?

One parameter at 100% schema coverage, so the schema already fully documents 'domain' with an example. The description adds only an indirect format hint through the sample output domain. Baseline 3 applies when the schema does the heavy lifting.

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 precise verb+resource: domain intelligence covering registrar, creation/expiry dates (RDAP/WHOIS), A/AAAA/MX/NS/TXT records, SPF, DMARC and HTTPS reachability 'in one call'. It enumerates the exact payload, so an agent knows what it gets. However, it never names the obvious sibling domain_lookup_batch (or ip_lookup/url_dossier), so single-vs-batch differentiation is left to inference.

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?

It names three concrete use cases – due diligence, phishing checks, lead research – which gives clear context for when to reach for it. There are no explicit exclusions or pointers to alternatives, so an agent must infer that bulk work belongs to domain_lookup_batch.

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