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p-l-ta

mail-mcp

by p-l-ta

list_senders

Read-only

Lists senders across mailboxes with message count, unread count, and last arrival time. Helps identify bulk senders and reduce noise by excluding system folders.

Instructions

Return a grouped count of senders across mailboxes — who sends how many messages, how many are unread, and when the last arrived. By default excludes Deleted Messages, Junk, Sent, and Drafts so counts reflect live mail only. Ideal for identifying bulk senders and noise.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoMax senders to return, ordered by message count desc
accountNoAccount host/name substring to restrict to
mailboxNoMailbox name or URL substring to restrict to (e.g. 'INBOX', 'Amtrak'). When omitted, covers all non-system mailboxes (see include_system_mailboxes).
include_system_mailboxesNoWhen true, include Deleted Messages, Junk, Sent Messages, and Drafts in unscoped queries. Has no effect when mailbox is specified.
Behavior4/5

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

The description discloses the default exclusion of system mailboxes (Deleted, Junk, Sent, Drafts), which is a behavioral detail beyond what annotations provide. This is valuable for understanding data scope.

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?

Two sentences with no filler. The first sentence front-loads the key output, the second adds important default behavior. Every word is useful.

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?

The description adequately covers the tool's purpose and key behavioral traits. While it lacks explicit output schema details, the described metrics (count, unread, last arrived) are sufficient for an AI agent to understand what the tool returns.

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?

Schema coverage is 100% and the description adds minimal new meaning beyond the parameter descriptions (e.g., limit default 50, mailbox substring). The description does not elaborate on parameter behavior beyond the schema.

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 it returns a grouped count of senders across mailboxes, specifying the exact metrics (count, unread, last arrived). This distinguishes it from sibling tools like list_recent or search_emails which focus on individual messages.

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 explicitly states it is ideal for identifying bulk senders and noise, providing clear context for when to use. However, it does not directly contrast with sibling tools or specify when not to use it.

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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