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DataNexus MCP

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by datanexusmcp
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Python
Hybrid

domain_check_email_security

Read-onlyIdempotent

Evaluate a domain's email security by checking SPF, DMARC, and DKIM records, returning an overall grade and detailed scores.

Instructions

Check SPF, DMARC, and DKIM email authentication for a domain.

domain: Domain without protocol e.g. "google.com".

Returns: overall_grade (A–F), spf_score, dmarc_score, dkim_score (each 0–10), spf_record, dmarc_record, dkim_selectors_found. Scores reflect live DNS via Cloudflare DoH — no cache.

SPF: -all=10 (strict), ~all=7, ?all=4, none=2, +all=0 (open relay). DMARC: p=reject=10, p=quarantine=7, p=none=4, absent=0; +1 for rua set. DKIM: selector found=10, none=0. Checks 10 common selectors in parallel.

Example: check_email_security(domain="google.com")

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
domainYesDomain without protocol e.g. google.com. Required.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changedv2.1.16
    • addedInput schema / properties / domain / description
      Added value: +"Domain without protocol e.g. google.com. Required."
  2. Addedv2.1.8
  3. Removedv2.1.7
  4. Addedv2.1.6

TDQS

A4.5/5.0
Behavior5/5

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

The description extensively elaborates on behavior beyond annotations: it uses live DNS via Cloudflare DoH with no cache, explains scoring details for SPF, DMARC, DKIM, and describes parallel checking of 10 selectors. This gives full transparency on how results are derived.

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

Conciseness5/5

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

The description is well-structured with a front-loaded purpose, followed by parameter and return format, then detailed scoring explanations. Every sentence adds value, and it is appropriately sized 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?

Given the tool has an output schema, the description complements it by explaining the scoring logic and DNS source. It covers all necessary aspects for an agent to invoke the tool correctly and interpret results.

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?

Schema description coverage is 100% and the description merely repeats the same information ('domain: Domain without protocol'). It adds no additional meaning beyond the schema, so baseline 3 is appropriate.

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 clearly states the tool checks SPF, DMARC, and DKIM email authentication for a domain. It uses specific verbs ('Check') and resource ('domain's email authentication'), and effectively distinguishes from sibling tools like domain_fetch_dns_records by focusing on email security.

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

Usage Guidelines4/5

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

The description provides clear context for when to use the tool (to assess a domain's email security posture) but does not explicitly mention when not to use it or suggest alternatives. The focus is strong, but lacks exclusion criteria.

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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