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dossier_dmarc

Core dossier check: Retrieve and parse a domain's DMARC policy from its _dmarc. TXT record, returning all tags. Use to audit email authentication policy, verify the p (policy) and rua (reporting) settings, or confirm alignment mode; pair with dossier_spf and dossier_dkim for complete email-auth coverage. Queries _dmarc. via Cloudflare DoH (1.1.1.1), 5 s timeout; parses each tag=value pair. Returns a CheckResult: on success, {status:"ok", raw, tags:{p, rua, ruf, adkim, aspf,...}}; on failure, {status:"error", reason}.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
domainYesPublic FQDN, e.g. example.com. Must be resolvable on the public internet; IPs, ports, paths, and protocol prefixes are rejected.

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. Changed1 schema field changed
    • changedInput schema / properties / domain / description
      Previous value: -"Public FQDN."New value: +"Public FQDN, e.g. example.com. Must be resolvable on the public internet; IPs, ports, paths, and protocol prefixes are rejected."
  2. Added

TDQS

A4.6/5.0
Behavior5/5

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

With no annotations provided, the description carries the full behavioral burden. It discloses the DNS-over-HTTPS endpoint (Cloudflare 1.1.1.1), 5-second timeout, tag=value parsing behavior, and the exact CheckResult return shape including success and error states. This is highly transparent for a DNS query tool and has no annotation contradictions.

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?

Three sentences with a logical flow: purpose, use cases, then mechanics and return format. It is information-dense but not bloated, and front-loads the core purpose before technical details.

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 single required parameter and no output schema, the description fully covers the invocation context. It explains the return contract (status, raw, tags, error reason), the query process, and the relationship to sibling tools, leaving no ambiguity for an agent to select and call 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% and already validates the domain parameter. The description adds meaning by explaining how the parameter is used to construct the _dmarc.<domain> query, and reinforces the public FQDN requirement. This goes beyond the schema's validation errors.

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 uses a specific verb+resource: 'Retrieve and parse a domain's DMARC policy from its _dmarc.<domain> TXT record, returning all tags.' It clearly distinguishes from siblings by mentioning pairing with dossier_spf and dossier_dkim for full email-auth coverage, and specifies the exact DNS resource queried.

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?

It explicitly states when to use: 'Use to audit email authentication policy, verify the p and rua settings, or confirm alignment mode,' and points to complementary tools (dossier_spf, dossier_dkim). However, it doesn't provide explicit exclusions or when-not-to-use scenarios, so it's clear but not exhaustive.

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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