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IntoDNS.ai DNS & Email Security Scanner

by ai.intodns

parse_dmarc_report

Read-onlyIdempotent

Read-only parser for a DMARC aggregate (RUA) XML report (RFC 7489). Turns the raw XML that mailbox providers send into structured JSON: report metadata (org, report id, date range), the published policy (p/sp/adkim/aspf/pct), and one row per sending source with source IP, message count, evaluated disposition (none/quarantine/reject), aligned SPF/DKIM results, and pass/fail totals. Provide the report as xml (raw text) or gzipBase64 (a base64-encoded .gz attachment). Use to programmatically read DMARC reports an agent fetched from the rua@ mailbox; the report is parsed in-memory and not stored. No auth, no side effects.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
xmlNoRaw DMARC aggregate report XML (root <feedback>), max 5 MB
gzipBase64NoBase64-encoded gzip of the report (.gz attachment); used when xml is omitted

TDQS

A4.5/5.0
Behavior5/5

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

Annotations already cover readOnly, idempotent, and non-destructive hints. The description adds valuable behavioral context beyond that: 'parsed in-memory and not stored' and 'No auth, no side effects', which fully disclose the tool's side-effect-free and stateless nature. This exceeds annotation coverage.

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?

Four sentences deliver purpose, input, output, and behavior with zero fluff. It is front-loaded with the key 'Read-only parser' phrase and each sentence adds information, creating a tight, well-structured description.

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?

No output schema exists, so the description carries the full burden of explaining what the tool returns. It does so in detail: report metadata (org, report id, date range), published policy (p/sp/adkim/aspf/pct), per-source rows with IP, count, disposition, SPF/DKIM results, and pass/fail totals. It also covers input formats and side effects, making it complete.

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 schema already has 100% coverage for both parameters (xml max 5 MB, gzipBase64 as base64 gzip). The description adds the relationship that only one is needed and identifies the XML as a DMARC report, but mostly restates schema details. With full schema coverage, the baseline of 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?

Clearly states it is a 'Read-only parser for a DMARC aggregate (RUA) XML report' (RFC 7489), naming the specific verb and resource. It enumerates the exact JSON output structure including metadata, policy fields, and per-source rows, and is readily distinguishable from sibling tools like check_dmarc or generate_dmarc.

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 explicit context: 'Use to programmatically read DMARC reports an agent fetched from the rua@ mailbox'. This makes the intended scenario clear. However, it does not explicitly name alternative tools or state when not to use it, so it misses the top score.

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

A4.1/5.0
Disambiguation5/5

Every tool has a clearly distinct purpose, and cross-references between tools (e.g., check_spf vs check_email_security vs check_sender_requirements) explicitly state when to use each one. Overlapping behaviors are carefully delineated (e.g., scan_domain vs get_everything_report vs start_deep_scan) with latency and depth tradeoffs explained. No two tools appear to do the same thing.

Naming Consistency4/5

Nearly all tool names follow a verb_noun snake_case pattern (check_*, generate_*, get_*, create_*, scan_*), with precise verbs matching each action. Minor deviations include 'nis2_quickscan' (no verb) and a few compound names like 'whois_lookup', but these are isolated. The variety of verbs is semantically justified by the broad domain, so the naming is predictable and readable.

Tool Count2/5

At 45 tools, this is far beyond the 16-25 'heavy' range and nearly double the 25 threshold. While the server covers a wide range of DNS, email, and web checks, a 45-tool surface is likely to overwhelm agents and increase selection errors. Many tools could be consolidated (e.g., individual check_* tools into one combined check) without sacrificing clarity.

Completeness5/5

The tool set meticulously covers the domain: DNS (SPF, DKIM, DMARC, DNSSEC, propagation, whois), email security (blacklist, FCrDNS, MTA-STS, SMTP TLS, TLSA, BIMI, raw email analysis, test sessions), web security (headers, CSP, HTTP/3), reporting (PDF, snapshots, badges), and compliance (NIS2, Internet.nl deep scans). There are no obvious gaps for the stated purpose of DNS & email security scanning; every check has a corresponding generator or explainer.

Resources