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dns-doctor

by dev.dnsdoctor

parse_dmarc_report

Read-onlyIdempotent

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.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

TDQS

A4.5/5.0
Behavior4/5

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

Beyond the annotations (readOnlyHint=true, destructiveHint=false), the description adds valuable behavioral context: 'Nothing is stored — the report is parsed and discarded' and the 2 MiB decoded size limit. This clarifies side effects and constraints, complementing the annotation-provided safety profile.

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 two sentences: the first states purpose and output, the second provides input instructions and a key behavioral note. Every sentence earns its place, with no冗余. It is front-loaded and easy to scan.

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's moderate complexity, the description covers input format, size constraints, output summary, and side effects. An output schema exists, so return values are already structured; the description's output summary is sufficient context. No significant gaps remain.

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% with descriptive parameter comments. The description adds further meaning by specifying that content_base64 expects encoded file bytes (not raw XML), the decoded size limit, and that filename only affects logs, not parsing. This enriches the schema with practical usage details.

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 verb 'Parse' with a specific resource 'DMARC aggregate (RUA) report' and defines the output as readable per-source aggregates (who sent mail, how much, alignment share). This distinguishes it from sibling tools like get_report, which likely fetches reports rather than parsing provided content.

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 gives clear invocation context: pass file bytes base64-encoded in content_base64, with optional filename. It also specifies supported formats and size limit. However, it does not explicitly name alternatives or when-not-to-use, though the instruction is sufficiently clear for the intended use case.

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

A4.4/5.0
Disambiguation5/5

Each tool targets a distinct DNS or email authentication task. Despite multiple DMARC-related tools, their purposes are clearly differentiated: build_dmarc_upgrade upgrades policies, generate_dmarc_record creates new ones, validate_dmarc_record validates pasted records, and scan_domain vs get_report differentiate fresh vs cached scans.

Naming Consistency5/5

All tool names follow a consistent verb_noun pattern in snake_case (e.g., build_dmarc_upgrade, check_record, scan_domain). No mixing of naming conventions, making the set predictable and easy to navigate.

Tool Count5/5

With 11 tools, the server is well-scoped for a DNS/email authentication diagnostics tool. The number covers essential operations without being overwhelming or sparse.

Completeness4/5

The tool set covers core workflows: DMARC management (generate, validate, upgrade, parse reports), DNS checks (propagation, reverse DNS, SPF lookups), and DKIM selector checks. Minor gaps exist, such as the absence of SPF validation or DKIM signature verification, but the main diagnostic and monitoring use cases are supported.

Resources