slack-mcp-server

jianrongzhang89/slack-mcp-server

3.1

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A specialized Model Context Protocol (MCP) server built with fastMCP that provides intelligent Slack integration with AI-powered natural language search capabilities.

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Slack MCP Server

A specialized Model Context Protocol (MCP) server built with fastMCP that provides intelligent Slack integration with AI-powered natural language search capabilities.

Features

Slack Integration Tools

  • slack_list_channels: List all accessible Slack channels
  • slack_get_channel_messages: Get recent messages from a specific channel
  • slack_search_messages: Search for messages across channels (with fallback implementation)
  • slack_smart_search: 🧠 AI-powered natural language search with intelligent query understanding
  • slack_get_user_info: Get information about a Slack user
  • slack_send_message: Send a message to a Slack channel

Development Tools

  • ping_test: Connection test tool (development server only)

Resources

  • slack://channels: List all accessible Slack channels as a resource
  • slack://status: Check Slack connection status as a resource

Installation

This project uses uv for dependency management.

  1. Clone the repository:

    git clone <your-repo-url>
cd slack-mcp-server

  2. Install dependencies:

    uv sync

  3. Install MCP CLI tools (for development):

    uv add "mcp[cli]"

  4. Install MCP Inspector (for testing):

    npm install -g @modelcontextprotocol/inspector

  5. Set up environment variables:

    # Required for Slack integration
export SLACK_BOT_TOKEN=xoxb-your-slack-bot-token-here
export SLACK_APP_TOKEN=xapp-your-slack-app-token-here

# Optional for enhanced AI features
export OPENAI_API_KEY=your-openai-api-key-here

Usage

Production Server

Run the production server with fastMCP:

uv run python main.py

Development Server with MCP Inspector

For development and testing, use the development server (main_dev.py) with the MCP Inspector:

Quick Setup (Recommended)

Use the provided setup script for automatic configuration:

# Run the setup script (creates wrapper and starts inspector)
./setup_inspector.sh

This script will:

  • Create a wrapper script in ~/bin/mcp-server-everything
  • Automatically start the MCP Inspector
  • Provide the inspector URL for connecting
Manual Setup

If you prefer manual setup:

  1. Set environment variables:

    export SLACK_BOT_TOKEN=xoxb-your-token-here
export SLACK_APP_TOKEN=xapp-your-app-token-here
export OPENAI_API_KEY=your-openai-key-here  # Optional

  2. Start the development server:

    uv run python main_dev.py

  3. In a separate terminal, start the MCP Inspector:

    npx @modelcontextprotocol/inspector

  4. Connect to the server:

    • Use the URL provided by the inspector (includes session token)
    • For local development, typically: http://localhost:3000

Connecting to the Server

The server implements the MCP protocol and can be connected to by any MCP-compatible client. The server will handle:

  • Tool discovery and execution
  • Resource access
  • Proper error handling and responses

Examples

Once connected through an MCP client, you can use the Slack tools:

  • Basic Slack Operations (requires SLACK_BOT_TOKEN):

    • List channels: slack_list_channels()
    • Get messages: slack_get_channel_messages("C1234567890", limit=20)
    • Search messages: slack_search_messages("deployment", channel_id="C1234567890")
    • Get user info: slack_get_user_info("U1234567890")
    • Send message: slack_send_message("C1234567890", "Hello from MCP!")

  • 🧠 AI-Powered Smart Search (enhanced with OPENAI_API_KEY):

    • Natural language search: slack_smart_search("Show me discussions about deployment from last week")
    • User-focused search: slack_smart_search("What did John say about the API changes?")
    • Topic analysis: slack_smart_search("Find decisions made about the mobile app")
    • Sentiment search: slack_smart_search("Show me concerns people raised about performance")

Resources

  • slack://channels: List all accessible Slack channels as a resource
  • slack://status: Check Slack connection status as a resource

Slack Integration Setup

Step 1: Create a Slack Bot

  1. Go to https://api.slack.com/apps

  2. Click "Create New App" → "From scratch"

  3. Give your app a name and select your workspace

  4. Go to "OAuth & Permissions" in the sidebar

  5. Add the following Bot Token Scopes:

    • channels:read (to list channels)
    • channels:history (to read channel messages)
    • groups:read (to list private channels)
    • groups:history (to read private channel messages)
    • chat:write (to send messages)
    • users:read (to get user information)
    • app_mentions:read (to receive mentions)
    • im:history (to read direct messages)
    • mpim:history (to read group direct messages)

    Note: search:read scope may not be available for all apps. The server includes a fallback search implementation that works without this scope.

Step 2: Install the Bot

  1. Click "Install to Workspace" and authorize the app
  2. Copy the "Bot User OAuth Token" (starts with xoxb-)
  3. Set the environment variable: 
    export SLACK_BOT_TOKEN=xoxb-your-token-here

Step 3: Invite Bot to Channels

Invite your bot to the channels you want it to access:

/invite @your-bot-name

Step 4: Test Integration

Start the server and the Slack tools will be available. The server will show whether Slack integration is enabled on startup.

AI Features

The smart search functionality has two modes:

Enhanced Mode (with OpenAI API Key)

  • Advanced natural language query understanding
  • Intelligent result summarization
  • Better handling of synonyms and context
  • Smart extraction of time, user, and content filters

Fallback Mode (without OpenAI API Key)

  • Basic keyword-based search with semantic similarity
  • Rule-based query parsing for time/user filters
  • Simple result counting

To enable enhanced mode, set your OpenAI API key:

export OPENAI_API_KEY=your-openai-api-key-here

Security

  • All Slack tools validate inputs and handle exceptions gracefully
  • Slack integration requires proper OAuth token setup with appropriate scopes
  • Slack API calls are rate-limited and include comprehensive error handling
  • AI search functionality respects Slack permissions and channel access
  • Bot tokens are securely managed through environment variables
  • No sensitive data is stored or logged

Development

To extend this Slack-focused server:

  1. Add new Slack tools using the @mcp.tool() decorator
  2. Add new Slack resources using the @mcp.resource() decorator
  3. Enhance AI search capabilities in ai_search.py
  4. Ensure proper error handling and type hints
  5. Test with an MCP client or the MCP Inspector

Contributing

  1. Fork the repository
  2. Create a feature branch
  3. Make your changes
  4. Test thoroughly with real Slack data
  5. Submit a pull request

License

MIT License