The Slack MCP Server enables AI assistants to interact with Slack's API through a standardized interface, providing tools for:
Message Management: Post messages to channels, reply to threads, add emoji reactions
Channel Operations: List public channels, get channel message history
User Information: Retrieve basic and detailed user profile information
Search Functionality: Find messages across the workspace with customizable filters
These capabilities allow AI assistants to effectively communicate and gather information within Slack workspaces.
Supports environment variable configuration through .env files for storing Slack tokens and other configuration settings.
Integrated for code linting during development to maintain code quality and consistency.
Hosts the package in GitHub Registry, requiring a Personal Access Token (PAT) for installation and access to the MCP server package.
Used as the package manager for installing and running the MCP server package.
Used for code formatting to ensure consistent code style across the project.
Provides tools for interacting with Slack API, including listing channels, posting messages, replying to threads, adding reactions, retrieving channel history, getting thread replies, retrieving user information, and searching messages in a Slack workspace.
Used for defining and validating request/response schemas, ensuring proper data structure for API interactions and limiting response fields to necessary data.
slack-mcp-server
A MCP(Model Context Protocol) server for accessing Slack API. This server allows AI assistants to interact with the Slack API through a standardized interface.
Transport Support
This server supports both traditional and modern MCP transport methods:
Stdio Transport (default): Process-based communication for local integration
Streamable HTTP Transport: HTTP-based communication for web applications and remote clients
Related MCP server: MCP Toolkit
Features
Available tools:
slack_list_channels- List public channels in the workspace with paginationslack_post_message- Post a new message to a Slack channelslack_reply_to_thread- Reply to a specific message thread in Slackslack_add_reaction- Add a reaction emoji to a messageslack_get_channel_history- Get recent messages from a channelslack_get_thread_replies- Get all replies in a message threadslack_get_users- Retrieve basic profile information of all users in the workspaceslack_get_user_profiles- Get multiple users' profile information in bulk (efficient for batch operations)slack_search_messages- Search for messages in the workspace with powerful filters:Basic query search
Location filters:
in_channelUser filters:
from_user,withDate filters:
before(YYYY-MM-DD),after(YYYY-MM-DD),on(YYYY-MM-DD),during(e.g., "July", "2023")Content filters:
has(emoji reactions),is(saved/thread)Sorting options by relevance score or timestamp
Quick Start
Installation
NOTE: Its now hosted in GitHub Registry so you need your PAT.
Configuration
You need to set the following environment variables:
SLACK_BOT_TOKEN: Slack Bot User OAuth TokenSLACK_USER_TOKEN: Slack User OAuth Token (required for some features like message search)SLACK_SAFE_SEARCH(optional): When set totrue, automatically excludes private channels, DMs, and group DMs from search results. This is enforced server-side and cannot be overridden by clients.
You can also create a .env file to set these environment variables:
Usage
Start the MCP server
Stdio Transport (default):
Streamable HTTP Transport:
You can also run the installed module with node:
Command Line Options:
-port <number>: Start with Streamable HTTP transport on specified port-h, --help: Show help message
Client Configuration
For Stdio Transport (Claude Desktop, etc.):
For Streamable HTTP Transport (Web applications):
Start the server:
Connect to: http://localhost:3000/mcp
See examples/README.md for detailed client examples.
Implementation Pattern
This server adopts the following implementation pattern:
Define request/response using Zod schemas
Request schema: Define input parameters
Response schema: Define responses limited to necessary fields
Implementation flow:
Validate request with Zod schema
Call Slack WebAPI
Parse response with Zod schema to limit to necessary fields
Return as JSON
For example, the slack_list_channels implementation parses the request with ListChannelsRequestSchema, calls slackClient.conversations.list, and returns the response parsed with ListChannelsResponseSchema.
Development
Available Scripts
npm run dev- Start the server in development mode with hot reloadingnpm run build- Build the project for productionnpm run start- Start the production servernpm run lint- Run linting checks (ESLint and Prettier)npm run fix- Automatically fix linting issues
Contributing
Fork the repository
Create your feature branch
Run tests and linting:
npm run lintCommit your changes
Push to the branch
Create a Pull Request