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TypeScript

parse_dmarc_report

Read-onlyIdempotent

Parse a DMARC aggregate (RUA) XML report to reveal which sources sent mail for your domain, how much, and the share aligned with SPF or DKIM. Reports are processed and discarded.

Instructions

Use this when the user uploads or pastes a DMARC aggregate (RUA) XML report and asks what it says. Parse ONE DMARC aggregate (RUA) report into readable per-source aggregates: who sent mail as the domain, how much, and what share was SPF/DKIM aligned. Pass the file's bytes base64-encoded in content_base64 (XML, .gz or .zip; up to 2 MiB decoded) with an optional filename. Nothing is stored — the report is parsed and discarded.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
filenameNoOptional original attachment filename, recorded in logs only — format detection is content-based, so this changes nothing about parsing.
content_base64YesOne DMARC aggregate (RUA) report file, base64-encoded: the .xml, .xml.gz or .zip attachment exactly as received, up to 2 MiB decoded. Encode the file bytes — do not paste raw XML here.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed2 schema fields changedv1.2.1
    • addedInput schema / properties / content_base64 / description
      Added value: +"One DMARC aggregate (RUA) report file, base64-encoded: the .xml, .xml.gz or .zip attachment exactly as received, up to 2 MiB decoded. Encode the file bytes — do not paste raw XML here."
    • addedInput schema / properties / filename / description
      Added value: +"Optional original attachment filename, recorded in logs only — format detection is content-based, so this changes nothing about parsing."
  2. First observedv0.1.0

TDQS

A4.6/5.0
Behavior4/5

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

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint false, and the description reinforces and extends this by stating 'Nothing is stored — the report is parsed and discarded.' It also discloses input format handling (XML, .gz, .zip), the 2 MiB limit, and content-based format detection, adding behavioral context beyond the annotations.

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 a compact paragraph with no filler. The use condition is front-loaded, followed by what the tool does, how to pass the input, and a closing safety note. Every sentence contributes essential operational detail.

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?

With no output schema, the description compensates by summarizing the return content: readable per-source aggregates covering sender, volume, and alignment share. It also covers input formats, size limits, storage behavior, and parameter semantics, making the tool fully invocable without further inference.

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

Parameters5/5

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

The schema already documents both parameters, but the description adds meaningful usage semantics: it instructs to base64-encode raw file bytes, warns against pasting raw XML, specifies 'exactly as received,' and clarifies that filename is only for logs and does not affect parsing. This materially helps an agent construct the correct invocation.

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 and resource: 'Parse ONE DMARC aggregate (RUA) report' and clearly defines the output as per-source aggregates, including sending domains, volume, and SPF/DKIM alignment shares. This distinguishes it from sibling tools like validate_dmarc_record or get_report, which address different DMARC operations.

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 opening line, 'Use this when the user uploads or pastes a DMARC aggregate (RUA) XML report and asks what it says,' provides a clear trigger condition. It does not explicitly name alternatives or exclusion criteria, but the context is specific enough to guide selection among the sibling tools.

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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