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Analyze email headers

analyze_email_headers
Read-only

Analyze raw email headers supplied by the user to investigate authentication or delivery issues. Returns the SPF, DKIM and DMARC results reported in those headers, alignment, the sending IP, whether that IP and the From domain are on free public blocklists (not Spamhaus), and detected issues. It does not independently verify DKIM signatures or establish the receiving provider's exact spam-filter decision. Provide headers only, not the message body.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "headers": "<headers>"
    },
    "name": "analyze_email_headers"
  }
}

Input Schema

NameRequiredDescriptionDefault
headersYesThe raw message headers, as copied from "Show original"

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.7/5.0
Behavior5/5

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

Annotations already cover the read-only and open-world safety profile. The description adds substantial behavioral context beyond annotations: exactly what results are returned (SPF, DKIM, DMARC, alignment, sending IP, specific blocklist checks, detected issues) and important limitations such as not independently verifying DKIM signatures or determining the receiving provider's exact spam-filter decision.

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 front-loaded with purpose, then returns, then limitations, then the input constraint. Each sentence adds distinct value, and there is no redundant or filler text.

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 must explain return values, and it does so thoroughly. Annotations cover safety, the schema covers the parameter, and the description supplies sufficient scope and caveats for an agent to call this tool 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%, so the single parameter is well documented by the schema itself. The description still adds meaningful usage guidance by clarifying that only headers should be provided, not the message body, which helps the agent avoid supplying the wrong input.

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 states a specific verb and resource: analyze raw email headers supplied by the user. It clearly scopes the tool to authentication and delivery investigations, which distinguishes it from sibling domain-level tools like diagnose_domain and get_dmarc_summary.

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 gives clear context for use: investigate authentication or delivery issues, and it specifies a constraint to provide headers only, not the message body. However, it does not explicitly name alternative tools or when not to use this one.

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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