Slack-MCP

This project is a Multi-Tool MCP (Model Context Protocol) server for Slack automation using the FastMCP library. It allows you to fetch and process Slack messages using AI agents.This project is great example to learn and Build Understanding reagarding MCPs.

📂 Folder Structure

slack-mcp/
├── main.py               # Main MCP tool server with multiple tools
├── .env                  # Store your SLACK_TOKEN safely here
├── smithery.yaml         # Configuration for Smithery deployment
├── README.md             # This file
├── uv-lock
└── pyproject.toml        # Python dependencies

Related MCP server: agenthub MCP

🚀 Tools in This MCP

1. get_recent_messages

Fetches the most recent messages from a given Slack channel.

• Inputs: channel_id, limit

• Returns: Formatted recent messages.

2. get_user_messages

Fetches messages from a specific user within a channel.

• Inputs: channel_id, username, limit

• Returns: Messages sent by the user.

3. summarize_recent_messages

Summarizes the last N messages from a channel using a connected LLM.

• Inputs: channel_id, limit

• Returns: AI-generated summary.

4. group_messages_by_user

Groups recent channel messages by sender.

• Inputs: channel_id, limit

• Returns: A grouped format like:

  @user1:
  - Message 1
  - Message 2
  
  @user2:
  - Message A

🚪 Slack OAuth Setup & Token Generation

1. Create a Slack App

• Go to: https://api.slack.com/apps

• Click Create New App > From Scratch

• Give it a name and choose your workspace

2. Set OAuth Scopes

In OAuth & Permissions, under Bot Token Scopes, add:

• channels:read

• channels:history

• users:read

• chat:write (optional for sending messages)

3. Install to Workspace

• Click Install App to Workspace

• Authorize permissions

• Copy the Bot User OAuth Token (starts with xoxb-...)

4. Save the Token

Create a .env file:

SLACK_TOKEN=xoxb-your-token-here

If you change scopes later:

• Revisit OAuth & Permissions

• Click Reinstall App to apply new scopes

✨ Testing the Tools

On MCP Inspector (Local)

1. Run the server:

python main.py

2. Use MCP Inspector to call tools and test output

mcp dev main.py

On Smithery (Cloud)

1. Push your code to GitHub

2. Deploy your MCP to Smithery

3. Use the "Run tool" tab to test individual tools

On Claude (Cloud Desktop)

You can connect these tools via Smithery to Claude workflows for real-time Slack insights and summaries.

🚜 Requirements

Install dependencies with:

uv pip install -r pyproject.toml

Or manually:

pip install fastmcp httpx python-dotenv

🔧 Environment File (.env)

SLACK_TOKEN=xoxb-123-your-token

🎉 Contributing

Pull requests are welcome. If you’d like to add new Slack tools (e.g., send_message, track_reactions, etc.), feel free to open an issue or PR.

📹 Video Demo On claude desktop:

https://github.com/user-attachments/assets/37d905a5-6382-40ce-9500-2dd744656546

🙏 Acknowledgments

• FastMCP by Model Context Protocol

• Smithery.ai for deploying and testing MCPs

• Slack Web API Docs: https://api.slack.com/

📊 Example Output

Grouped Slack Messages:

@bakar:
- Just pushed latest PR
- Working on the backend API

@hamza:
- Updated Figma designs
- Fixed navbar bug

📊 Smithery.ai (Cloud Deployment)

Here is the URL go and checkout this mcp_server: Slack_MCP_Server

🧑‍💻 Author

Built by Abu Bakar Yasir — Computer Engineering @ COMSATS + AI Software Engineer | Full Stack Developer 🚀