Email infrastructure audit. Checks SPF, DKIM (10 selectors), DMARC, MX records, blacklist status (8 DNSBLs), and DNSSEC. Returns deliverability score (0-100). All DNS-based, zero API keys.
| Name | Required | Description | Default |
|---|---|---|---|
| domain | Yes | Domain to audit (e.g., gmail.com) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden. It adds meaningful behavioral context by stating 'All DNS-based, zero API keys' (indicating no external dependencies) and specifies the exact checks and score range. It does not disclose potential timeouts or rate limits, but for a read-only audit, this is reasonably transparent.
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, front-loaded with the core purpose, then enumerates concrete checks and output. Every sentence earns its place with no filler or redundant wording.
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 simplicity (1 parameter, no output schema, no annotations), the description covers the essential aspects: what it checks, what it returns, and its operational nature. It could have mentioned that the domain must be a public domain or that DNS lookups may be slow, but the information provided is largely sufficient for an agent to select and 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?
The single parameter 'domain' is already well-described in the schema with an example. The description does not add independent parameter-level meaning beyond what the schema provides, so the baseline score of 3 is appropriate.
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 performs an 'Email infrastructure audit' and lists the specific record checks (SPF, DKIM, DMARC, MX, DNSBL, DNSSEC) and output (deliverability score). This distinguishes it from sibling tools like verify_email (which verifies individual addresses) and domain_infra (which may cover broader infrastructure).
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 to audit a domain's email infrastructure—but provides no explicit guidance on when not to use it or how it differs from alternatives like domain_infra or site_audit. The 'All DNS-based, zero API keys' note offers some context but no comparative exclusions.
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.
Several tools have overlapping functionality, such as domain_infra, company_report, and due_diligence all covering DNS/WHOIS/SSL checks. Similarly, web_scrape and scrape_structured both extract website content, and verify_email overlaps with email_audit on DNS-based email checks. This creates ambiguity in tool selection, especially for agents looking for a specific type of analysis.
All tool names use lowercase with underscores, which provides a consistent style. However, the grammatical pattern varies: some are verb-object (verify_email, currency_convert), some are noun-noun (domain_infra, site_audit), and others are adjective-noun (arabic_sentiment, brand_scout). This is not chaotic, but it lacks a rigid verb_noun convention, making it slightly less predictable.
With 25 tools, this sits at the upper boundary of what is considered 'heavy' but is still usable. The server covers a wide range of domains (Arabic NLP, web scraping, domain/email analysis, finance, faith), so the count is justified to a degree, but agents may be overwhelmed by choice. It is borderline appropriate for such a broad utility server.
The tool surface covers many common operations (scraping, DNS checks, email verification, financial data), but there are notable gaps. For example, no generic translation tool exists, only Arabizi-to-Arabic, and there is no text generation or embedding. While the set is extensive, it is not fully comprehensive for the diverse domains it touches, leaving some obvious missing operations.