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Glama

Get messages sent to the bot

get_inbox

Retrieve direct messages and @mentions delivered to the Slack bot since this server process started; filter by unread status, channel, or time and mark them as read.

Instructions

Return direct messages and @mentions the bot received while this MCP server was running. Only covers events delivered to this process: messages sent before it started, or delivered to another running instance of the same Slack app, are not here. To reliably fetch replies in a known channel or thread, use wait_for_reply or conversations_history.

Input Schema

NameRequiredDescriptionDefault
limitNoMaximum number of messages (most recent kept)
sinceNoOnly messages after this time: Slack ts, ISO date, or relative (30m, 2h)
channelNoOnly messages from this channel or DM (ID, #name, or user)
mark_readNoMark returned messages as read
unread_onlyNoOnly messages not returned by a previous get_inbox call

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.6.1

TDQS

A4.2/5.0
Behavior4/5

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

Adds substantial behavior the annotations don't convey: the inbox is process-local and volatile, so history is incomplete by design. It does not explain why readOnlyHint=false, namely that mark_read defaults to true and mutates read state — that justification lives only in the schema.

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?

Three tight sentences: capability first, caveat second, alternative routing third. Every sentence carries distinct information and the most decision-relevant constraint is front-loaded.

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?

Covers the critical risk for this tool — that results are an incomplete, process-scoped window — which is what an agent most needs to avoid misinterpreting an empty inbox. With no output schema, the return shape is only loosely implied, but message lists are conventional enough that this is a minor gap.

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 description coverage is 100%, so all five parameters (limit, since, channel, mark_read, unread_only) are already documented with formats and defaults. The description adds no parameter-level detail, which meets the baseline for fully covered schemas but no more.

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?

States a specific verb and resource ('Return direct messages and @mentions the bot received') plus the precise scope ('while this MCP server was running'). It also names the sibling tools that serve related needs, so an agent can distinguish it from conversations_history and wait_for_reply without opening any schema.

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?

Explicitly states the exclusion case (messages before startup or delivered to another instance are absent) and routes the agent to wait_for_reply or conversations_history for reliable replies. It does not mention check_replies, a sibling that sounds closely related, leaving a small gap in the alternative-selection guidance.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.