slack-mcp

slack-mcp

A Model Context Protocol (MCP) server that enables AI assistants to interact with Slack workspaces. This server provides a bridge between AI tools and Slack, allowing you to read messages, post content, and manage Slack channels programmatically through MCP-compatible clients.

What is this and why should I use it?

This MCP server transforms your Slack workspace into an AI-accessible environment. Instead of manually switching between your AI assistant and Slack, you can now:

• Read channel messages - Get real-time updates and conversation history
• Post messages and commands - Send text, files, or execute Slack commands
• Manage reactions - Add emoji reactions to messages
• Join channels - Automatically join new channels as needed
• Thread conversations - Maintain context in threaded discussions

Key Benefits

• Seamless Integration: Connect your AI assistant directly to Slack without manual copy-pasting
• Automated Workflows: Build AI-powered Slack bots that can read, analyze, and respond to messages
• Enhanced Productivity: Let AI help manage notifications, summarize conversations, or automate routine Slack tasks
• Real-time Collaboration: Enable AI assistants to participate in team discussions and provide instant insights

Use Cases

• Team Assistant: Have an AI that can read team updates and provide summaries
• Notification Manager: Automatically categorize and respond to incoming messages
• Knowledge Base: AI that can search through channel history and provide context
• Meeting Scheduler: AI that can read meeting requests and help coordinate schedules

Setting Up with Claude Code

This repo ships as a Claude Code plugin with a guided setup skill. Claude will walk you through the entire process — no manual config editing required.

Run the setup script. It handles everything — venv, Playwright, token extraction, wrapper script, and Claude Code registration. The only interaction required is logging in to Slack when the browser opens, and entering a channel ID for server logs.

python3 <(curl -fsSL https://raw.githubusercontent.com/redhat-community-ai-tools/slack-mcp/main/scripts/setup-slack-mcp.py)

Or clone the repo first and run it locally:

git clone https://github.com/redhat-community-ai-tools/slack-mcp
python3 slack-mcp/scripts/setup-slack-mcp.py

Options:

Flag	Description
--logs-channel DXXXXXXXXX	Slack channel ID for server logs (prompted if omitted)
--workspace https://myco.slack.com	Specific Slack workspace to open
--refresh-tokens	Re-extract tokens when they expire (skips all other steps)
--skip-verify	Skip the post-setup smoke test

When tokens expire, just run:

python3 slack-mcp/scripts/setup-slack-mcp.py --refresh-tokens

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Running with Podman or Docker

You can run the slack-mcp server in a container using Podman or Docker:

Example configuration for running with Podman:

{
  "mcpServers": {
    "slack": {
      "command": "podman",
      "args": [
        "run",
        "-i",
        "--rm",
        "-e", "SLACK_XOXC_TOKEN",
        "-e", "SLACK_XOXD_TOKEN",
        "-e", "MCP_TRANSPORT",
        "-e", "LOGS_CHANNEL_ID",
        "quay.io/redhat-ai-tools/slack-mcp"
      ],
      "env": {
        "SLACK_XOXC_TOKEN": "xoxc-...",
        "SLACK_XOXD_TOKEN": "xoxd-...",
        "MCP_TRANSPORT": "stdio",
        "LOGS_CHANNEL_ID": "C7000000"
      }
    }
  }
}

Running with non-stdio transport

To run the server with a non-stdio transport (such as SSE), set the MCP_TRANSPORT environment variable to a value other than stdio (e.g., sse).

Example configuration to connect to a non-stdio MCP server:

{
  "mcpServers": {
    "slack": {
      "url": "https://slack-mcp.example.com/sse",
      "headers": {
        "X-Slack-Web-Token": "xoxc-...",
        "X-Slack-Cookie-Token": "xoxd-..."
      }
    }
  }
}

Extract your Slack XOXC and XOXD tokens easily using browser extensions or Selenium automation: https://github.com/maorfr/slack-token-extractor.