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Slack MCP

Slack MCP

by korotovsky
OverviewSchemaSourceRelated ServersDiscussionsAdmin
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Hybrid

channels_list

Retrieve and sort a list of Slack channels by type and popularity using this tool, designed for Slack MCP integration without requiring admin permissions or bot setups.

Instructions

Get list of channels

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": {
      "channel_types": "<channel_types>"
    },
    "name": "channels_list"
  }
}

Input Schema

NameRequiredDescriptionDefault
channel_typesYesPossible channel types. Allowed values: 'mpim', 'im', 'public_channel', 'private_channel'.
sortNoType of sorting. Allowed values: 'popularity' - sort by number of members/participants in each channel.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.0.0

TDQS

C2.7/5.0
Behavior2/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It only states the action ('Get list') without addressing permissions, rate limits, pagination, or what 'list' entails (e.g., format, completeness). This is inadequate for a tool that likely interacts with a chat system, where such details are critical.

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 extremely concise with just three words, front-loaded with the core action. There's no wasted text, making it efficient for quick understanding, though this brevity contributes to gaps in other dimensions.

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

Completeness2/5

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

Given no annotations and no output schema, the description is incomplete. It lacks details on behavioral traits (e.g., safety, performance), output format, and usage context. For a tool with parameters and likely complex interactions in a chat system, this minimal description fails to provide sufficient context for effective agent use.

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 the schema fully documents both parameters (channel_types and sort). The description adds no parameter-specific information beyond what's in the schema, meeting the baseline score of 3 for high schema coverage without additional value.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose3/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description 'Get list of channels' clearly states the verb ('Get') and resource ('channels'), but it's vague about scope and doesn't distinguish from the sibling tool 'conversations_history'. It doesn't specify whether this retrieves all channels, user-accessible channels, or some subset, leaving purpose ambiguous beyond the basic action.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

No guidance is provided on when to use this tool versus alternatives like 'conversations_history'. The description doesn't mention context, prerequisites, or exclusions, leaving the agent to infer usage based solely on the tool name and parameters.

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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