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List emails

list_emails
Read-only

List emails the business has sent, newest first, with their delivery state and times. Covers the last 30 days unless days/from-date say otherwise. Filter by status, kind or related entity, and page with cursor.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
daysNoHow far back to look, in days. Defaults to 30; pass 0 to read the whole history (slower).
kindNoOnly emails of this kind (e.g. "invoice", "reminder", "quote-accepted", "order-received").
limitNoPage size; defaults to 50, capped at 250.
queryNoCase-insensitive substring match on the subject line.
cursorNoOffset from the previous response's `cursor`; omit for the first page.
statusNoOnly emails in this delivery state: sent | delivered | bounced | complained | rejected | delayed | failed.
to-dateNoOnly emails sent at or before this epoch-millis timestamp.
contextidNoOnly emails about this entity — e.g. an invoice id, to see that invoice's send history.
from-dateNoOnly emails sent at or after this epoch-millis timestamp. Overrides `days`.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.6/5.0
Behavior5/5

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

Beyond the readOnlyHint annotation, the description discloses behavioral traits: default 30-day time window, override via days/from-date, sorting newest first, filtering by status/kind/entity, and cursor pagination. This adds meaningful operational context without contradicting annotations.

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 three sentences, front-loaded with the core purpose, and every sentence adds functional detail (ordering, time window, filters, pagination). No wasted words.

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?

With 9 optional parameters and no output schema, the description provides a solid overview including defaults and pagination. It does not describe the exact return format, but that is typical for list tools and the schema covers parameter details. Slightly more on response shape would push it to 5.

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 parameters are individually documented. The description adds value by clarifying relationships: the 30-day default overridden by days/from-date, and cursor for pagination. This is beyond the schema's per-parameter descriptions.

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 action (list) and the resource (emails the business has sent), with specific details on ordering (newest first) and included data (delivery state and times). This distinguishes it from sibling list tools like list_invoices or list_documents, and from get_email for single records.

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 context on when to use the tool: to list sent emails with filtering and pagination. It does not explicitly name alternatives or exclusions (e.g., 'use get_email for a single email'), so it lacks explicit when-not guidance, but the purpose is unambiguous.

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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