domain-health-checkerBulk-audit domains: DNS records, SSL certificate expiry, SPF & DMARC email authentication. Find domains that cannot receive email, are easy to spoof, or have expiring certificates — before your clients do. — $0.01/call, x402 (USDC on base).
| Name | Required | Description | Default |
|---|---|---|---|
| domains | Yes | List of domains to audit (e.g. `example.com`). | |
| maxConcurrency | No | How many domains to check in parallel. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and destructiveHint=false, so the safety profile is covered. The description adds value by specifying exactly what is audited (DNS, SSL, SPF/DMARC) and by disclosing the cost and payment method ($0.01/call, x402 USDC on base). This goes beyond the annotations without contradicting them.
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: the first states the core function, the second conveys the value proposition, and the third provides essential cost information. It is front-loaded and every sentence earns its place with no filler.
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?
Given the tool's complexity (bulk domain auditing with multiple checks) and lack of an output schema, the description adequately conveys the purpose, input scope, and cost. It does not describe the response format or error handling, but the description is sufficient for an agent to decide whether to invoke it.
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 input schema has 100% description coverage for both parameters (domains and maxConcurrency). The description does not add any additional meaning to these parameters, only indirectly implying the domain list via 'Bulk-audit domains.' Per the rubric, the baseline of 3 applies when schema already documents parameters well.
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 and resource: "Bulk-audit domains: DNS records, SSL certificate expiry, SPF & DMARC email authentication." This clearly distinguishes it from sibling tools like tech-stack-detector or url-to-markdown, which focus on different aspects.
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 provides clear context for when to use: "Find domains that cannot receive email, are easy to spoof, or have expiring certificates — before your clients do." However, it does not explicitly name alternatives or state when not to use, so it falls short of a 5.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
There are multiple overlapping tool pairs: tech-stack-detector vs tech-stack-change-detector (the latter is a superset that includes detection), and url-to-markdown vs structured-extract vs sitemap-to-knowledge (all fetch web pages and convert content, differing only in output format). These boundaries are unclear, and an agent could easily select the wrong one without deep reading of descriptions.
Naming is a mix of hyphenated descriptors (domain-health-checker, tech-stack-detector), snake_case (pricing_info), and verb phrases (structured-extract, url-to-markdown). The pattern is inconsistent: some tools are named after the action (extract, convert), others after the target (shopify-store-intelligence). This makes it hard to predict tool names.
With 10 tools, the count is well within the 3-15 ideal range. Each tool addresses a distinct web intelligence need (domain health, tech stack, e-commerce, content extraction, pricing), and none are purely redundant filler. The scale feels appropriate for the server's stated purpose.
The surface covers core web intelligence workflows well: domain auditing, tech stack detection (with change detection), content extraction, and e-commerce monitoring for Shopify and Zid. Minor gaps exist, such as missing generic e-commerce platform coverage or a dedicated WHOIS lookup, but these are not critical given the existing domain-health-checker.