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secops-field-notes

Official
by labaccessnow

analyze_email_headers

Triage phishing by analyzing email headers: trace Received hops, check SPF/DKIM/DMARC verdicts, and identify domain mismatches that reveal spoofing or BEC.

Instructions

Phishing triage on raw email headers: the Received chain in chronological order with per-hop delay, the originating public IP, SPF/DKIM/DMARC verdicts, and the mismatches that matter — From vs Return-Path domain, Reply-To pointing elsewhere (BEC), authentication failures, hops timestamped before the previous one.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
headersYesThe raw message source, or at least the header block.

Schema Changelog

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

  1. First observedv0.1.0

TDQS

A4.3/5.0
Behavior4/5

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

With no annotations, the description carries the behavioral disclosure burden and does so well: it enumerates the analysis outputs, ordering of the Received chain, per-hop delays, and specific anomaly checks. It does not mention return format or error behavior, but the disclosed output contract is unusually rich.

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?

One dense, front-loaded sentence delivers the purpose first and then a structured list of concrete outputs. Every clause adds useful information, with no filler or repetition.

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?

Despite having no output schema, the description effectively serves as the return contract by detailing exactly what analysis results and flagged anomalies will be produced. Combined with a single fully documented parameter, an agent has enough information to invoke and interpret the tool correctly.

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 only parameter, headers, already has a 100% descriptive schema ('The raw message source, or at least the header block'). The tool description reaffirms that same concept without adding new parameter-level details, so the baseline score 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?

The description identifies a specific resource (raw email headers) and a precise task (phishing triage), then enumerates concrete outputs such as the Received chain, originating IP, and authentication verdicts. This clearly distinguishes the tool from its unrelated siblings.

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 context is explicit: this is for phishing triage on raw email headers. It does not explicitly state when not to use it or name alternatives, but the sibling tools are unrelated enough that the intended use case is clear.

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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