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Email MX

email_mx
Read-onlyIdempotent

Analyze email security: MX records, SPF policy, DMARC policy, DKIM probe across common+date-based selectors, mail provider, grade. Use to verify email-auth setup and phishing risk; for full audit use domain_report. Free: 30/hr, Pro: 500/hr. email_security.dkim_status reports honest evidence: 'verified' iff at least one selector responded, else 'unverifiable' (custom selectors cannot be discovered without prior knowledge). Grade: when DKIM verified, A=SPF+DMARC+DKIM/B=2of3/C=1of3; when DKIM unverifiable, A=SPF+DMARC/B=one/F=neither — DKIM absence is NOT penalized because it is unprovable in DNS. Returns {mx_records, mail_provider, email_security:{spf, dmarc, dkim_selectors, dkim_status, grade, issues}, summary}.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
domainYesDomain to analyze email configuration for (e.g. 'example.com', 'google.com')

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

TDQS

A4.6/5.0
Behavior5/5

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

Goes far beyond the read-only annotation by explaining the honest DKIM evidence policy (verified only if a selector responds, else unverifiable), the grading logic's dependence on DKIM verifiability, and that DKIM absence is not penalized. This prevents misinterpretation of results. Rate limits are also disclosed.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is dense and front-loaded with the core purpose, then adds necessary detail on DKIM semantics, grading rules, and return structure. It is somewhat long but every sentence carries distinct value; the semicolon-separated lists keep it readable. Not overly verbose for the complexity.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Covers the return object shape, the DKIM evidence edge case, the grading algorithm in detail, rate limits, and directs to a more comprehensive sibling. With an output schema present, the description still adds substantial context, making the tool fully self-contained for an agent.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The single parameter 'domain' is already fully described in the schema with examples. The description adds no new parameter syntax or constraints, so baseline 3 is appropriate given 100% schema coverage.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description states a specific action ('Analyze email security') followed by the exact resources analyzed (MX, SPF, DMARC, DKIM, provider, grade). It names sibling tool domain_report to draw the boundary, making its purpose unambiguous.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicitly states when to use it ('verify email-auth setup and phishing risk') and directs users to domain_report for a full audit. Also includes rate-limit context, helping agents decide whether to call it under resource constraints.

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

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TDQS

A4.5/5.0
Disambiguation4/5

Most tools have clearly distinct purposes, with differences between lookup/search/scan/audit for each domain. However, some overlap exists (e.g., email_mx vs email_security_posture, scan_headers vs contrast_scan) which could cause occasional confusion. Overall, boundaries are well-defined.

Naming Consistency5/5

Tool names follow a consistent verb_noun pattern (e.g., cve_lookup, check_headers, bulk_cve_lookup) with all lowercase underscores. Variations like kev_detail or ssl_check are minor and still predictable. No chaotic mixing of conventions.

Tool Count4/5

54 tools is high but justified by the broad cybersecurity scope (CVE, ATLAS, D3FEND, Sigma, domain, email, IOC, scanning). Some redundancy exists (e.g., three email-related tools), but the count is not excessive given the API's comprehensive feature set.

Completeness5/5

The tool set thoroughly covers the threat intelligence and domain investigation lifecycle: CVE/KEV/exploit/CWE, ATLAS/D3FEND/Sigma, DNS/WHOIS/SSL/subdomains, email security, IOC enrichment, and active scanning. No significant gaps are apparent for the stated cybersecurity purpose.