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Proxy Mail QA

Record what an account agreed to

record_consent
Idempotent

Records what the address agreed to at sign-up, after you filled in the form: marketing (opted_in or declined), prechecked (whether the consent box was already ticked when the page loaded) and double_opt_in (confirmed or pending, when the app asks to confirm the subscription by email; it confirms itself when the verify link is used). check_email then judges marketing email by the rules where the persona lives (create_address with country): marketing_consent, prechecked_consent, double_opt_in, unsubscribe_deadline, postal_address, sender_identity, minor_marketing, misleading_subject and ad_label, for the EU and EEA, UK, US, Canada, Australia, New Zealand, Switzerland, Japan, South Korea, Singapore and Hong Kong, each naming its official source. A test aid, not legal advice. Give only what changed; answers with the consent as recorded. In Labs: works once the person who connected you switches on Marketing and offer emails in Account settings → Labs; until then it answers with an error that says so.

Input Schema

NameRequiredDescriptionDefault
atNoWhen. Default: now.
addressYesA test address in this project. A variant works too (capitals, or a +tag such as name+o'brien@…): it acts on the plain address, and a wait on a +tag variant only finds mail sent to it.
marketingNoWhat it chose about marketing email.
precheckedNoThe marketing box was ticked already when the form loaded.
double_opt_inNoWhether it has confirmed its subscription by email.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Added

TDQS

A4.2/5.0
Behavior4/5

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

Annotations already declare the tool is non-read-only, idempotent, and non-destructive. The description adds important behavioral details beyond annotations: the Labs feature gate (must enable 'Marketing and offer emails' or it returns an error), partial-update semantics ('Give only what changed'), and the return format ('answers with the consent as recorded').

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness3/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is front-loaded with purpose but then devotes several sentences to check_email's jurisdiction coverage and output fields, which are tangential to this tool. This extra detail dilutes focus and could be trimmed for better conciseness.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the mutation tool's complexity (5 parameters, 1 required), full schema coverage, and no output schema, the description covers purpose, usage sequence, feature gate, and return behavior adequately. It is complete enough for an agent to invoke it 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 description coverage is 100%, so the baseline is 3. The description adds value by explaining double_opt_in self-confirmation ('it confirms itself when the verify link is used') and by clarifying that only changed fields need to be supplied, which affects how parameters are used in practice.

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 first sentence states a specific verb and resource: 'Records what the address agreed to at sign-up' and lists the consent fields (marketing, prechecked, double_opt_in). It also distinguishes this tool from the sibling check_email by noting that check_email 'judges' the recorded consent, making the division of labor clear.

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 provides clear usage context: 'after you filled in the form' and 'check_email then judges', indicating the sequence and purpose. It lacks explicit exclusions or alternatives, but the implied workflow is sufficient for correct selection.

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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Listed in ChatGPT · Proxy Mail QA

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