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Generate DMARC Record

generate_dmarc_record
Read-onlyIdempotent

Create a DMARC record for domains without one. A validating engine builds and re-checks the record for policy, reporting, and subdomain settings, ready for human approval.

Instructions

Use this when the user asks to create, generate or write a DMARC record for a domain that has none. Build a DMARC record from scratch for a domain that has none, using a validating engine — never compose one yourself. policy is none|quarantine|reject; optional rua_email (aggregate-report destination), subdomain_policy, and strict_alignment. Every generated record carries np=reject — the DMARCbis tag for non-existent subdomains, which can have no legitimate aligned mail — independently of the p= you choose. The generated record is re-validated before it is returned. Present it verbatim; a human must approve before publishing.

Input Schema

NameRequiredDescriptionDefault
policyYesThe requested p= policy: 'none' monitors only, 'quarantine' sends failing mail to spam, 'reject' refuses it outright. Start at 'none' unless the domain's aggregate reports already justify enforcement.
rua_emailNoMailbox to receive DMARC aggregate (RUA) reports, as a plain address like dmarc@example.com. Strongly recommended: without it nobody can see who sends as the domain.
strict_alignmentNoSet true to emit strict alignment (aspf=s adkim=s), requiring an exact domain match instead of the organizational-domain match. Leave false unless you know every sender aligns strictly.
subdomain_policyNoOptional sp= policy for subdomains when it should differ from the main p= policy. Omit to let subdomains inherit p=.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed4 schema fields changedv1.2.1
    • addedInput schema / properties / policy / description
      Added value: +"The requested p= policy: 'none' monitors only, 'quarantine' sends failing mail to spam, 'reject' refuses it outright. Start at 'none' unless the domain's aggregate reports already justify enforcement."
    • addedInput schema / properties / rua_email / description
      Added value: +"Mailbox to receive DMARC aggregate (RUA) reports, as a plain address like dmarc@example.com. Strongly recommended: without it nobody can see who sends as the domain."
    • addedInput schema / properties / strict_alignment / description
      Added value: +"Set true to emit strict alignment (aspf=s adkim=s), requiring an exact domain match instead of the organizational-domain match. Leave false unless you know every sender aligns strictly."
    • addedInput schema / properties / subdomain_policy / description
      Added value: +"Optional sp= policy for subdomains when it should differ from the main p= policy. Omit to let subdomains inherit p=."
  2. First observedv0.1.0

TDQS

A4.7/5.0
Behavior5/5

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

Annotations already declare the safe read-only, idempotent, non-destructive profile, and the description adds substantial context on top: generation runs through a validating engine, the record is re-validated before return, every output carries np=reject regardless of the chosen p=, the output must be presented verbatim, and a human must approve before publishing.

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?

Front-loads the usage condition, then the mechanism, then the parameter map, then the invariant (np=reject) and the operational constraint. Every sentence carries information an agent needs; nothing is filler.

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?

There is no output schema, and the description compensates by describing what the generated record is (validated, np=reject-bearing) and what to do with it (present verbatim, human approval). Combined with annotations covering the safety profile, an agent has everything needed to invoke it correctly.

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

Parameters4/5

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

Schema coverage is 100%, so the baseline is 3, but the description adds real meaning: it enumerates the policy enum, names the three optional parameters, and clarifies that the np=reject tag is emitted independently of the agent-selected p= policy — a constraint the schema does not express.

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?

States a specific verb and resource (build a DMARC record from scratch) and scopes it with a discriminating condition: 'for a domain that has none.' That condition separates it from validate_dmarc_record and build_dmarc_upgrade without naming them.

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?

Gives an explicit trigger ('when the user asks to create, generate or write a DMARC record for a domain that has none') and an exclusion implied by that clause, plus the instruction to never hand-compose one. It stops short of naming the sibling tools an agent should route to when the domain already has a record.

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