Slack MCP Client in Go

This project provides a Slack bot client that serves as a bridge between Slack and Model Context Protocol (MCP) servers. By leveraging Slack as the user interface, it allows LLM models to interact with multiple MCP servers using standardized MCP tools.

Overview

This project implements a Slack bot client that acts as a bridge between Slack and Model Context Protocol (MCP) servers. It uses Slack as a user interface while enabling LLM models to communicate with various MCP servers through standardized MCP tools.

Important distinction: This client is not designed to interact with the Slack API directly as its primary purpose. However, it can achieve Slack API functionality by connecting to a dedicated Slack MCP server (such as modelcontextprotocol/servers/slack) alongside other MCP servers.

How It Works

Image

  1. User interacts only with Slack, sending messages through the Slack interface

  2. Slack Bot Service is a single process that includes:

    • The Slack Bot component that handles Slack messages

    • The MCP Client component that communicates with MCP servers

  3. The MCP Client forwards requests to the appropriate MCP Server(s)

  4. MCP Servers execute their respective tools and return results

Features

  • ✅ Multi-Mode MCP Client:

    • Server-Sent Events (SSE) for real-time communication

    • HTTP transport for JSON-RPC

    • stdio for local development and testing

  • ✅ Slack Integration:

    • Uses Socket Mode for secure, firewall-friendly communication

    • Works with both channels and direct messages

    • Rich message formatting with Markdown and Block Kit

    • Automatic conversion of quoted strings to code blocks for better readability

  • ✅ Multi-Provider LLM Support:

    • OpenAI (GPT-4, GPT-4o, etc.)

    • Anthropic (Claude 3.5 Sonnet, etc.)

    • Ollama (Local LLMs like Llama, Mistral, etc.)

    • Factory pattern for easy provider switching

    • LangChain gateway for unified API

  • ✅ Custom Prompt Engineering:

    • System Prompts - Define custom AI assistant behavior and personality

  • ✅ RAG (Retrieval-Augmented Generation):

    • LangChain Go Compatible - Drop-in replacement for standard vector stores

    • Document Processing - PDF ingestion with intelligent chunking

    • CLI Tools - Command-line utilities for document management

    • Extensible Design - Easy to add SQLite, Redis, or vector embeddings

  • ✅ Tool Registration: Dynamically register and call MCP tools

  • ✅ Configuration Management:

    • JSON-based MCP server configuration

    • Environment variable support

    • Multiple transport modes (HTTP/SSE, stdio)

  • ✅ Production Ready:

    • Docker container support

    • Kubernetes Helm charts

    • Comprehensive logging and error handling

    • 88%+ test coverage

Installation

From Binary Release

Download the latest binary from the GitHub releases page or install using Go:

# Install latest version using Go
go install github.com/tuannvm/slack-mcp-client@latest

# Or build from source
git clone https://github.com/tuannvm/slack-mcp-client.git
cd slack-mcp-client
make build
# Binary will be in ./bin/slack-mcp-client

Running Locally with Binary

After installing the binary, you can run it locally with the following steps:

  1. Set up environment variables:

# Using environment variables directly
export SLACK_BOT_TOKEN="xoxb-your-bot-token"
export SLACK_APP_TOKEN="xapp-your-app-token"
export OPENAI_API_KEY="sk-your-openai-key"
export OPENAI_MODEL="gpt-4o"
export LOG_LEVEL="info"

# Or create a .env file and source it
cat > .env << EOL
SLACK_BOT_TOKEN="xoxb-your-bot-token"
SLACK_APP_TOKEN="xapp-your-app-token"
OPENAI_API_KEY="sk-your-openai-key"
OPENAI_MODEL="gpt-4o"
LOG_LEVEL="info"
EOL

source .env

  1. Create an MCP servers configuration file:

# Create mcp-servers.json in the current directory
cat > mcp-servers.json << EOL
{
  "mcpServers": {
    "filesystem": {
      "command": "npx",
      "args": ["-y", "@modelcontextprotocol/server-filesystem", "$HOME"],
      "env": {}
    }
  }
}
EOL

  1. Run the application:

# Run with default settings (looks for mcp-servers.json in current directory)
slack-mcp-client

# Or specify a custom config file location
slack-mcp-client --config /path/to/mcp-servers.json

# Enable debug mode
slack-mcp-client --debug

# Specify OpenAI model
slack-mcp-client --openai-model gpt-4o-mini

The application will connect to Slack and start listening for messages. You can check the logs for any errors or connection issues.

RAG Setup and Usage

The client includes an improved RAG (Retrieval-Augmented Generation) system that's compatible with LangChain Go and provides professional-grade performance:

Quick Start with RAG

  1. Enable RAG in your configuration:

{
  "llm_provider": "openai",
  "llm_providers": {
    "openai": {
      "type": "openai", 
      "model": "gpt-4o",
      "rag_config": {
        "enabled": true,
        "provider": "json",
        "database_path": "./knowledge.json",
        "chunk_size": 1000,
        "chunk_overlap": 200,
        "max_results": 10
      }
    }
  }
}

  1. Ingest documents using CLI:

# Ingest PDF files from a directory
slack-mcp-client --rag-ingest ./company-docs --rag-db ./knowledge.json

# Test search functionality
slack-mcp-client --rag-search "vacation policy" --rag-db ./knowledge.json

# Get database statistics
slack-mcp-client --rag-stats --rag-db ./knowledge.json

  1. Use in Slack:

Once configured, the LLM can automatically search your knowledge base:

User: "What's our vacation policy?"

AI: "Let me search our knowledge base for vacation policy information..." (Automatically searches RAG database)

AI: "Based on our company policy documents, you get 15 days of vacation..."

RAG Features

  • 🎯 Smart Search: Advanced relevance scoring with word frequency, filename boosting, and phrase matching

  • 🔗 LangChain Compatible: Drop-in replacement for standard vector stores

  • 📈 Extensible: Easy to add vector embeddings and other backends

Custom Prompts and Assistants

The client supports advanced prompt engineering capabilities for creating specialized AI assistants:

System Prompts

Create custom AI personalities and behaviors:

# Create a custom system prompt file
cat > sales-assistant.txt << EOL
You are SalesGPT, a helpful sales assistant specializing in B2B software sales.

Your expertise includes:
- Lead qualification and discovery
- Solution positioning and value propositions  
- Objection handling and negotiation
- CRM best practices and sales processes

Always:
- Ask qualifying questions to understand prospect needs
- Provide specific, actionable sales advice
- Reference industry best practices
- Maintain a professional yet friendly tone

When discussing pricing, always emphasize value over cost.
EOL

# Use the custom prompt
slack-mcp-client --system-prompt ./sales-assistant.txt

Configuration-Based Prompts

Define prompts in your configuration:

{
  "llm_provider": "openai",
  "llm_providers": {
    "openai": {
      "type": "openai",
      "model": "gpt-4o",
      "system_prompt": "You are a helpful DevOps assistant specializing in Kubernetes and cloud infrastructure.",
      "conversation_starters": [
        "Help me debug a Kubernetes pod issue",
        "Explain best practices for CI/CD pipelines", 
        "Review my Dockerfile for optimization"
      ]
    }
  }
}

Assistant Roles

Create specialized assistants for different use cases:

  • DevOps Assistant: Kubernetes, Docker, CI/CD expertise

  • Sales Assistant: Lead qualification, objection handling

  • HR Assistant: Policy questions, onboarding guidance

  • Support Assistant: Customer issue resolution

  • Code Review Assistant: Security, performance, best practices

Kubernetes Deployment with Helm

For deploying to Kubernetes, a Helm chart is available in the helm-chart directory. This chart provides a flexible way to deploy the slack-mcp-client with proper configuration and secret management.

Installing from GitHub Container Registry

The Helm chart is also available directly from GitHub Container Registry, allowing for easier installation without needing to clone the repository:

# Add the OCI repository to Helm (only needed once)
helm registry login ghcr.io -u USERNAME -p GITHUB_TOKEN

# Pull the Helm chart
helm pull oci://ghcr.io/tuannvm/charts/slack-mcp-client --version 0.1.0

# Or install directly
helm install my-slack-bot oci://ghcr.io/tuannvm/charts/slack-mcp-client --version 0.1.0 -f values.yaml

You can check available versions by visiting the GitHub Container Registry in your browser.

Prerequisites

  • Kubernetes 1.16+

  • Helm 3.0+

  • Slack Bot and App tokens

Basic Installation

# Create a values file with your configuration
cat > values.yaml << EOL
secret:
  create: true

env:
  SLACK_BOT_TOKEN: "xoxb-your-bot-token"
  SLACK_APP_TOKEN: "xapp-your-app-token"
  OPENAI_API_KEY: "sk-your-openai-key"
  OPENAI_MODEL: "gpt-4o"
  LOG_LEVEL: "info"

# Optional: Configure MCP servers
configMap:
  create: true
EOL

# Install the chart
helm install my-slack-bot ./helm-chart/slack-mcp-client -f values.yaml

Configuration Options

The Helm chart supports various configuration options including:

  • Setting resource limits and requests

  • Configuring MCP servers via ConfigMap

  • Managing sensitive data via Kubernetes secrets

  • Customizing deployment parameters

For more details, see the Helm chart README.

Using the Docker Image from GHCR

The Helm chart uses the Docker image from GitHub Container Registry (GHCR) by default. You can specify a particular version or use the latest tag:

# In your values.yaml
image:
  repository: ghcr.io/tuannvm/slack-mcp-client
  tag: "latest"  # Or use a specific version like "1.0.0"
  pullPolicy: IfNotPresent

To manually pull the image:

# Pull the latest image
docker pull ghcr.io/tuannvm/slack-mcp-client:latest

# Or pull a specific version
docker pull ghcr.io/tuannvm/slack-mcp-client:1.0.0

If you're using private images, you can configure image pull secrets in your values:

imagePullSecrets:
  - name: my-ghcr-secret

Docker Compose for Local Testing

For local testing and development, you can use Docker Compose to easily run the slack-mcp-client along with additional MCP servers.

Setup

  1. Create a .env file with your credentials:

# Create .env file from example
cp .env.example .env
# Edit the file with your credentials
nano .env

  1. Create a mcp-servers.json file (or use the example):

# Create mcp-servers.json from example
cp mcp-servers.json.example mcp-servers.json
# Edit if needed
nano mcp-servers.json

  1. Start the services:

# Start services in detached mode
docker-compose up -d

# View logs
docker-compose logs -f

# Stop services
docker-compose down

Docker Compose Configuration

The included docker-compose.yml provides:

  • Environment variables loaded from .env file

  • Volume mounting for MCP server configuration

  • Examples of connecting to additional MCP servers (commented out)

version: '3.8'

services:
  slack-mcp-client:
    image: ghcr.io/tuannvm/slack-mcp-client:latest
    container_name: slack-mcp-client
    environment:
      - SLACK_BOT_TOKEN=${SLACK_BOT_TOKEN}
      - SLACK_APP_TOKEN=${SLACK_APP_TOKEN}
      - OPENAI_API_KEY=${OPENAI_API_KEY}
      - OPENAI_MODEL=${OPENAI_MODEL:-gpt-4o}
    volumes:
      - ./mcp-servers.json:/app/mcp-servers.json:ro

You can easily extend this setup to include additional MCP servers in the same network.

Slack App Setup

  1. Create a new Slack app at https://api.slack.com/apps

  2. Enable Socket Mode and generate an app-level token

  3. Add the following Bot Token Scopes:

    • app_mentions:read

    • chat:write

    • im:history

    • im:read

    • im:write

    • users:read

    • users.profile:read

  4. Enable Event Subscriptions and subscribe to:

    • app_mention

    • message.im

  5. Install the app to your workspace

For detailed instructions on Slack app configuration, token setup, required permissions, and troubleshooting common issues, see the Slack Configuration Guide.

LLM Integration

The client supports multiple LLM providers through a flexible integration system:

LangChain Gateway

The LangChain gateway enables seamless integration with various LLM providers:

  • OpenAI: Native support for GPT models (default)

  • Ollama: Local LLM support for models like Llama, Mistral, etc.

  • Extensible: Can be extended to support other LangChain-compatible providers

LLM-MCP Bridge

The custom LLM-MCP bridge layer enables any LLM to use MCP tools without requiring native function-calling capabilities:

  • Universal Compatibility: Works with any LLM, including those without function-calling

  • Pattern Recognition: Detects when a user prompt or LLM response should trigger a tool call

  • Natural Language Support: Understands both structured JSON tool calls and natural language requests

Configuration

LLM providers can be configured via environment variables or command-line flags:

# Set OpenAI as the provider (default)
export LLM_PROVIDER="openai"
export OPENAI_MODEL="gpt-4o"

# Use Anthropic
export LLM_PROVIDER="anthropic"
export ANTHROPIC_API_KEY="your-anthropic-api-key"
export ANTHROPIC_MODEL="claude-3-5-sonnet-20241022"

# Or use Ollama
export LLM_PROVIDER="ollama"
export LANGCHAIN_OLLAMA_URL="http://localhost:11434"
export LANGCHAIN_OLLAMA_MODEL="llama3"

Switching Between Providers

You can easily switch between providers by changing the LLM_PROVIDER environment variable:

# Use OpenAI
export LLM_PROVIDER=openai

# Use Anthropic
export LLM_PROVIDER=anthropic

# Use Ollama (local)
export LLM_PROVIDER=ollama

Configuration

The client uses two main configuration approaches:

Environment Variables

Configure LLM providers and Slack integration using environment variables:

Variable
Description
Default

SLACK_BOT_TOKEN

Bot token for Slack API

(required)

SLACK_APP_TOKEN

App-level token for Socket Mode

(required)

OPENAI_API_KEY

API key for OpenAI authentication

(required)

OPENAI_MODEL

OpenAI model to use

gpt-4o

ANTHROPIC_API_KEY

API key for Anthropic authentication

(required for Anthropic)

ANTHROPIC_MODEL

Anthropic model to use

claude-3-5-sonnet-20241022

LOG_LEVEL

Logging level (debug, info, warn, error)

info

LLM_PROVIDER

LLM provider to use (openai, anthropic, ollama)

openai

LANGCHAIN_OLLAMA_URL

URL for Ollama when using LangChain

http://localhost:11434

LANGCHAIN_OLLAMA_MODEL

Model name for Ollama when using LangChain

llama3

MCP Server Configuration

MCP servers are configured via a JSON configuration file (default: mcp-servers.json):

{
  "mcpServers": {
    "filesystem": {
      "command": "npx",
      "args": ["-y", "@modelcontextprotocol/server-filesystem", "/path/to/directory"],
      "env": {}
    },
    "github": {
      "command": "github-mcp-server",
      "args": ["stdio"],
      "env": {
        "GITHUB_PERSONAL_ACCESS_TOKEN": "your-token"
      }
    },
    "web-api": {
      "mode": "http",
      "url": "http://localhost:8080/mcp",
      "initialize_timeout_seconds": 30
    }
  }
}

For detailed configuration options, see the Implementation Notes.

Slack-Formatted Output

The client includes a comprehensive Slack-formatted output system that enhances message display in Slack:

  • Automatic Format Detection: Automatically detects message type (plain text, markdown, JSON Block Kit, structured data) and applies appropriate formatting

  • Markdown Formatting: Supports Slack's mrkdwn syntax with automatic conversion from standard Markdown

    • Converts **bold** to *bold* for proper Slack bold formatting

    • Preserves inline code, block quotes, lists, and other formatting elements

  • Quoted String Enhancement: Automatically converts double-quoted strings to inline code blocks for better visualization

    • Example: "namespace-name" becomes `namespace-name` in Slack

    • Improves readability of IDs, timestamps, and other quoted values

  • Block Kit Integration: Converts structured data to Block Kit layouts for better visual presentation

    • Automatically validates against Slack API limits

    • Falls back to plain text if Block Kit validation fails

For more details, see the Slack Formatting Guide.

Transport Modes

The client supports three transport modes:

  • SSE (default): Uses Server-Sent Events for real-time communication with the MCP server

  • HTTP: Uses HTTP POST requests with JSON-RPC for communication

  • stdio: Uses standard input/output for local development and testing

Documentation

Comprehensive documentation is available in the docs/ directory:

Configuration & Setup

  • Slack Configuration Guide - Complete guide for setting up your Slack app, including required permissions, tokens, and troubleshooting common issues

Development & Implementation

  • Implementation Notes - Detailed technical documentation covering the current architecture, core components, and implementation details

  • Requirements Specification - Comprehensive requirements documentation including implemented features, quality requirements, and future enhancements

User Guides

  • Slack Formatting Guide - Complete guide to message formatting including Markdown-to-Slack conversion, Block Kit layouts, and automatic format detection

  • RAG Implementation Guide - Detailed guide for the improved RAG system with LangChain Go compatibility and performance optimizations

  • RAG SQLite Implementation - Implementation plan for native Go SQLite integration with ChatGPT-like upload experience

  • Testing Guide - Comprehensive testing documentation covering unit tests, integration tests, manual testing procedures, and debugging

Quick Links

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

License

This project is licensed under the MIT License - see the LICENSE file for details.

CI/CD and Releases

This project uses GitHub Actions for continuous integration and GoReleaser for automated releases.

Continuous Integration Checks

Our CI pipeline performs the following checks on all PRs and commits to the main branch:

Code Quality

  • Linting: Using golangci-lint to check for common code issues and style violations

  • Go Module Verification: Ensuring go.mod and go.sum are properly maintained

  • Formatting: Verifying code is properly formatted with gofmt

Security

  • Vulnerability Scanning: Using govulncheck to check for known vulnerabilities in dependencies

  • Dependency Scanning: Using Trivy to scan for vulnerabilities in dependencies

  • SBOM Generation: Creating a Software Bill of Materials for dependency tracking

Testing

  • Unit Tests: Running tests with race detection and code coverage reporting

  • Build Verification: Ensuring the codebase builds successfully

Release Process

When changes are merged to the main branch:

  1. CI checks are run to validate code quality and security

  2. If successful, a new release is automatically created with:

    • Semantic versioning based on commit messages

    • Binary builds for multiple platforms

    • Docker image publishing to GitHub Container Registry

    • Helm chart publishing to GitHub Container Registry

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