Skip to main content
Glama
oviron

slack-personal-mcp

by oviron

slack_channel_info

Read-only

Retrieve Slack channel details—name, type, topic, purpose, member count, and your read marker—using a workspace and channel ID. Requires read scopes for that conversation type.

Instructions

Name, kind, topic, purpose, member count and your read marker via conversations.info; needs channels:read/groups:read/im:read/mpim:read for the conversation type.

Input Schema

NameRequiredDescriptionDefault
workspaceYes
channel_idYes

Output Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.2.3

TDQS

B3.4/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=true and openWorldHint=true, so the description earns credit for adding what annotations cannot: the specific OAuth scopes required and the fact that the needed scope varies with the conversation type (channel/group/im/mpim). That is actionable pre-flight information an agent would otherwise only discover from an auth failure.

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

Conciseness4/5

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

Two dense clauses with zero filler: the returned fields first, then the auth precondition. It is telegraphic to the point of being fragmentary, but every phrase carries information and the structure is front-loaded.

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

Completeness3/5

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

An output schema exists, so return values need no prose, and the auth scopes are covered. What remains missing is anything about the two required inputs — particularly how workspace is identified and whether channel_id type must be known in advance — which is material for a lookup tool that can't be called correctly without that.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters2/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0% and neither parameter has a title beyond the raw field name, so the description carries the full burden and falls short: it never explains what 'workspace' means (slug, ID, or name) or that 'channel_id' must match the conversation type implied by the scopes. Only the loosest hint ('for the conversation type') gestures at the ID's semantics.

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

Purpose4/5

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

The description names the operation precisely by enumerating the fields returned (name, kind, topic, purpose, member count, read marker) and cites the underlying API call (conversations.info), which tells an agent this is a single-conversation lookup rather than a search. It does not explicitly contrast with siblings like slack_find_channel, but the field list makes the distinction inferable.

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

Usage Guidelines3/5

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

Usage is only implied: the mention of conversation-type-specific scopes hints that the caller must already know the conversation type, and by extension that this tool operates on a known channel_id. There is no explicit statement of when to prefer this over slack_find_channel or slack_read_channel, nor any when-not guidance.

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