Slack MCP Client

A production-ready bridge between Slack and AI models with full MCP compatibility.

This client enables AI models (OpenAI GPT-4.1, Anthropic Claude 4.5, Ollama local models) to interact with real tools and systems through Slack conversations. Built on the industry-standard Model Context Protocol (MCP), it provides secure access to filesystems, databases, Kubernetes clusters, Git repositories, and custom tools.

Compatible with MCP Specification 2025-06-18 - Compliant with the latest Model Context Protocol standards

Oct 2025: langchaingo v0.1.14 with streaming fixes, enhanced agent parsing, and API key sanitization.

• Universal MCP Compatibility - Supports all transport methods (HTTP, SSE, stdio)
• Multi-Provider LLM Support - OpenAI GPT-4.1/4o, Anthropic Claude 4.5, Ollama (Llama 3.3, Qwen, Mistral, DeepSeek)
• Agent Mode - Multi-step reasoning with LangChain for complex workflows
• RAG Integration - Knowledge base with semantic search capabilities
• Thread-Aware Context - Maintains separate conversation history per Slack thread
• User Context Integration - Personalized responses with cached user information
• Unique Tool Naming - Server-prefixed tool names prevent conflicts across MCP servers
• Production Ready - Comprehensive configuration, monitoring, and security

• DevOps Teams - Infrastructure automation and monitoring through Slack
• Development Teams - Code review, Git operations, and file management
• Support Teams - Database queries, system status checks, and troubleshooting
• General Use - AI assistance with actual tools and system integration

Compliant with the official Model Context Protocol (2025-06-18 specification):

• All Transport Methods - HTTP, SSE, and stdio protocols
• JSON-RPC 2.0 - Standard communication protocol
• Official MCP Servers - Compatible with all modelcontextprotocol/servers
• Custom MCP Servers - Works with any MCP-compliant server
• Security Standards - Implements user consent, data privacy, and tool safety requirements

Authentication with Server-Sent Events (SSE) MCP servers can be achieved using the following setup:

Example:

{
  "httpHeaders": {
    "Authorization": "Bearer YOUR_TOKEN_HERE"
  }
}

Make sure to replace YOUR_TOKEN_HERE with your actual token for authentication.

flowchart LR
    User([👤 User]) --> Slack{🔗 Slack Interface}

    subgraph Infrastructure[Observability]
        Config[📋 Unified Config<br/>JSON Schema]
        Monitoring[📊 Monitoring<br/>Prometheus Metrics]
        Tracing[🔍 OpenTelemetry Tracing<br/>Langfuse & OTLP]
        Logging[📝 Structured Logging<br/>Debug & Analytics]
    end
    
    subgraph Core[Features]
        Slack --> Bridge[🌉 LLM-MCP Bridge<br/>Orchestration Layer]
        
        subgraph LLM[🤖 AI Processing]
            Bridge --> LLMRegistry[LLM Provider Registry]
            LLMRegistry --> OpenAI[OpenAI<br/>GPT-4o]
            LLMRegistry --> Anthropic[Anthropic<br/>Claude]
            LLMRegistry --> Ollama[Ollama<br/>Local Models]
            
            Bridge --> Agent{🎯 Agent Mode?}
            Agent -->|Yes| LangChain[🔄 LangChain Agent<br/>Multi-step Reasoning]
            Agent -->|No| Standard[⚡ Standard Mode<br/>Single Response]
        end
        
        subgraph Knowledge[📚 Knowledge & Memory]
            Bridge --> RAG[🧠 RAG System]
            RAG --> SimpleRAG[📄 JSON Store<br/>Simple Documents]
            RAG --> VectorRAG[🔍 OpenAI Vector Store<br/>Semantic Search]
        end
        
        subgraph Tools[🛠️ MCP Mode]
            Bridge --> MCPManager[MCP Client]
            MCPManager --> FileSystem[📁 Filesystem MCP Server<br/>Read/Write Files]
            MCPManager --> Git[🌿 Git MCP Server <br/>Repository Tools]
            MCPManager --> Kubernetes[☸️ Kubernetes MCP Server<br/>Cluster Management]
        end
    end
    
    
    Config -.-> Core
    Core -.-> Monitoring
    Core -.-> Tracing
    Core -.-> Logging
    
    style Core fill:#F8F9FA,stroke:#6C757D,stroke-width:3px
    style LLM fill:#E3F2FD,stroke:#1976D2,stroke-width:2px
    style Knowledge fill:#E8F5E8,stroke:#388E3C,stroke-width:2px
    style Tools fill:#FFF3E0,stroke:#F57C00,stroke-width:2px
    style Infrastructure fill:#F3E5F5,stroke:#7B1FA2,stroke-width:2px
    
    style User fill:#4CAF50,stroke:#2E7D32,stroke-width:2px,color:#fff
    style Slack fill:#4A90E2,stroke:#1565C0,stroke-width:2px,color:#fff
    style Bridge fill:#FF9800,stroke:#E65100,stroke-width:2px,color:#fff
    style LangChain fill:#9C27B0,stroke:#4A148C,stroke-width:2px,color:#fff
    style RAG fill:#2196F3,stroke:#0D47A1,stroke-width:2px,color:#fff

1. User interacts through Slack, sending messages that trigger intelligent AI workflows
2. LLM-MCP Bridge serves as the intelligent orchestration layer that:
   • Routes requests to appropriate LLM providers (OpenAI, Anthropic, Ollama)
   • Chooses between Agent Mode (multi-step reasoning) or Standard Mode (single response)
   • Integrates RAG system for knowledge retrieval and context enhancement
   • Manages tool discovery and execution across multiple MCP servers
3. Knowledge & Memory system provides contextual intelligence:
   • Simple JSON store for lightweight document storage
   • OpenAI Vector Store for semantic search and enterprise-grade RAG
4. Tool Ecosystem connects to diverse external systems:
   • Filesystem operations for file management
   • Git integration for repository interactions
   • Kubernetes cluster management and monitoring
   • Custom tools via HTTP, SSE, or stdio protocols
5. Infrastructure ensures production-ready deployment:
   • Unified JSON configuration with environment variable support
   • Prometheus metrics for observability and monitoring
   • OpenTelemetry tracing with Langfuse and OTLP providers
   • Structured logging for debugging and analytics

• ✅ Multi-Mode MCP Client:
  • Server-Sent Events (SSE) for real-time communication with automatic retry
  • 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
  • Thread-aware conversation tracking with separate context per thread
  • User context caching for personalized interactions
  • Customizable bot behavior and message history
• ✅ Multi-Provider LLM Support:
  • OpenAI (GPT-4.1, GPT-4o, o3-pro)
  • Anthropic (Claude Sonnet 4.5, Opus 4.1)
  • Ollama (Llama 3.3, Qwen2.5, Mistral, DeepSeek)
  • Native tool calling and unified LangChain gateway
• ✅ Agent Mode:
  • Autonomous AI agents powered by LangChain (langchaingo v0.1.14)
  • Enhanced multi-step reasoning and tool orchestration
  • Improved parsing for complex multi-line tool calls
  • Configurable agent iterations and behavior
  • Reliable streaming responses with memory leak fixes
  • Advanced prompt engineering capabilities
• ✅ RAG (Retrieval-Augmented Generation):
  • Multiple providers: Simple JSON storage, OpenAI Vector Store
  • Reusable vector stores with vectorStoreId support
  • Configurable search parameters and similarity metrics
  • PDF ingestion with intelligent chunking
  • CLI tools for document management
• ✅ Unified Configuration:
  • Single JSON configuration file with JSON schema validation
  • Comprehensive timeout and retry configuration
  • Environment variable substitution and overrides
  • All underlying package options exposed
  • Smart defaults with full customization capability
  • Server-prefixed tool names to prevent naming conflicts
• ✅ Production Ready:
  • Docker container support with GHCR publishing
  • Kubernetes Helm charts with OCI registry
  • Comprehensive logging and error handling
  • Test coverage with security scanning
• ✅ Monitoring & Observability:
  • Prometheus metrics integration
  • Tool invocation tracking with error rates
  • LLM token usage monitoring by model and type
  • OpenTelemetry tracing with Langfuse and simple providers
  • Configurable observability providers with graceful fallbacks
  • Comprehensive span tracking for LLM operations and tool calls
  • Configurable metrics endpoint and logging levels

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

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-4.1"  # or gpt-4o, o3-pro
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

2. Create a unified configuration file:

# Create config.json with the new unified configuration format
cat > config.json << EOL
{
  "\$schema": "https://github.com/tuannvm/slack-mcp-client/schema/config-schema.json",
  "version": "2.0",
  "slack": {
    "botToken": "\${SLACK_BOT_TOKEN}",
    "appToken": "\${SLACK_APP_TOKEN}"
  },
  "llm": {
    "provider": "openai",
    "useNativeTools": true,
    "providers": {
      "openai": {
        "model": "gpt-4o",
        "apiKey": "\${OPENAI_API_KEY}",
        "temperature": 0.7
      }
    }
  },
  "mcpServers": {
    "filesystem": {
      "command": "npx",
      "args": ["-y", "@modelcontextprotocol/server-filesystem", "\$HOME"]
    }
  },
  "monitoring": {
    "enabled": true,
    "metricsPort": 8080,
    "loggingLevel": "info"
  },
  "observability": {
    "enabled": true,
    "provider": "simple-otel",
    "endpoint": "${OTEL_EXPORTER_OTLP_ENDPOINT}",
    "serviceName": "slack-mcp-client",
    "serviceVersion": "1.0.0"
  }
}
EOL

3. Run the application:

# Run with unified configuration (looks for config.json in current directory)
slack-mcp-client --config config.json

# Enable debug mode with structured logging
slack-mcp-client --config config.json --debug

# Validate configuration before running
slack-mcp-client --config-validate --config config.json

# Configure metrics port via config file or flag
slack-mcp-client --config config.json --metrics-port 9090

If you have an existing mcp-servers.json file from a previous version, you can migrate to the new unified configuration format:

# Automatic migration (recommended)
slack-mcp-client --migrate-config --config legacy-mcp-servers.json --output config.json

# Manual migration: Use examples as templates
cp examples/minimal.json config.json
# Edit config.json with your specific settings

# Validate the new configuration
slack-mcp-client --config-validate --config config.json

The new configuration format provides:

• Single File: All settings in one config.json file
• JSON Schema: IDE support with autocomplete and validation
• Environment Variables: Use ${VAR_NAME} syntax for secrets
• Smart Defaults: Minimal configuration required for basic usage
• Comprehensive Options: All underlying package settings exposed

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

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

1. Enable RAG in your configuration:

{
  "$schema": "https://github.com/tuannvm/slack-mcp-client/schema/config-schema.json",
  "version": "2.0",
  "slack": {
    "botToken": "${SLACK_BOT_TOKEN}",
    "appToken": "${SLACK_APP_TOKEN}"
  },
  "llm": {
    "provider": "openai",
    "useNativeTools": true,
    "providers": {
      "openai": {
        "model": "gpt-4o",
        "apiKey": "${OPENAI_API_KEY}"
      }
    }
  },
  "rag": {
    "enabled": true,
    "provider": "simple",
    "chunkSize": 1000,
    "providers": {
      "simple": {
        "databasePath": "./knowledge.json"
      },
      "openai": {
        "indexName": "my-knowledge-base",
        "vectorStoreId": "vs_existing_store_id",
        "dimensions": 1536,
        "maxResults": 10
      }
    }
  }
}

2. 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

3. 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..."

• 🎯 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

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

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

Define prompts in your configuration:

{
  "$schema": "https://github.com/tuannvm/slack-mcp-client/schema/config-schema.json",
  "version": "2.0",
  "slack": {
    "botToken": "${SLACK_BOT_TOKEN}",
    "appToken": "${SLACK_APP_TOKEN}"
  },
  "llm": {
    "provider": "openai",
    "useNativeTools": true,
    "customPrompt": "You are a helpful DevOps assistant specializing in Kubernetes and cloud infrastructure.",
    "providers": {
      "openai": {
        "model": "gpt-4.1",
        "apiKey": "${OPENAI_API_KEY}",
        "temperature": 0.7
      }
    }
  }
}

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

Agent Mode enables more interactive and context-aware conversations using LangChain's agent framework. Instead of single-prompt interactions, agents can engage in multi-step reasoning, use tools more strategically, and maintain better context throughout conversations.

Agent Mode uses LangChain's conversational agent framework to provide:

1. Interactive Conversations: Maintains context across multiple exchanges
2. Strategic Tool Usage: Agents decide when and how to use available tools
3. Multi-Step Reasoning: Can break down complex problems into manageable steps
4. Streaming Responses: Provides real-time updates during processing
5. User Context Integration: Incorporates cached user information for personalized responses
6. Thread Context Awareness: Maintains separate conversation history per Slack thread

Enable Agent Mode in your configuration file:

{
  "$schema": "https://github.com/tuannvm/slack-mcp-client/schema/config-schema.json",
  "version": "2.0",
  "slack": {
    "botToken": "${SLACK_BOT_TOKEN}",
    "appToken": "${SLACK_APP_TOKEN}"
  },
  "llm": {
    "provider": "openai",
    "useNativeTools": true,
    "useAgent": true,
    "customPrompt": "You are a DevOps expert specializing in Kubernetes and cloud infrastructure. Always think through problems step by step.",
    "maxAgentIterations": 20,
    "providers": {
      "openai": {
        "model": "gpt-4.1",
        "apiKey": "${OPENAI_API_KEY}",
        "temperature": 0.7
      }
    }
  },
  "mcpServers": {
    "filesystem": {
      "command": "npx",
      "args": ["-y", "@modelcontextprotocol/server-filesystem", "/path/to/project"]
    },
    "github": {
      "command": "github-mcp-server",
      "args": ["stdio"],
      "env": {
        "GITHUB_PERSONAL_ACCESS_TOKEN": "${GITHUB_TOKEN}"
      }
    }
  }
}

• llm.useAgent: Enable agent mode (default: false)
• llm.useNativeTools: Use native LangChain tools vs system prompt-based tools (default: false)
• llm.customPrompt: System prompt for agent behavior
• llm.maxAgentIterations: Maximum agent reasoning steps (default: 20)

Standard Mode:

• Single-prompt interactions
• Tools described in system prompt as JSON schemas
• Direct tool call parsing and execution
• More predictable token usage
• Simpler conversation flow

Agent Mode:

• Multi-turn conversational interactions
• Context-aware tool usage decisions
• Better user context integration
• More natural conversation flow
• Enhanced reasoning capabilities

Interactive Development Consultation:

User: "I need help optimizing my React app performance"

Agent Response:
🤖 I'd be happy to help optimize your React app performance! Let me understand your current setup better.

[Agent maintains conversation context and asks relevant follow-up questions]
Agent: "What specific performance issues are you experiencing? Are you seeing slow renders, large bundle sizes, or something else?"

User: "The app takes too long to load initially"

Agent: "Let me check your current bundle setup and suggest optimizations..."
[Agent uses filesystem tools to analyze the project structure and provides targeted advice]

Contextual Problem Solving:

User: "Can you help me with my deployment pipeline?"

Agent Response:
🤖 I'll help you with your deployment pipeline. Since I know you're working on a React project, let me check your current CI/CD setup.

[Agent leverages previous conversation context and user information to provide personalized assistance]
[Agent strategically uses relevant tools based on the conversation flow]

1. System Prompts: Design clear, specific system prompts that guide the agent's behavior
2. Tool Selection: Provide relevant tools for the agent's domain
3. Context Management: Agents maintain better context across conversations
4. User Personalization: Leverage user context integration for personalized responses
5. Tool Strategy: Choose between native tools or system prompt-based tools based on your needs

• OpenAI Agent: Native OpenAI agent in langchaingo has known issues, uses conversational agent as workaround
• LangChain Dependency: Agent mode requires LangChain provider
• Permissions: May require additional Slack permissions for user information retrieval
• Performance: Agent mode may have different performance characteristics than standard mode

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.

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.

• Kubernetes 1.16+
• Helm 3.0+
• Slack Bot and App tokens

# 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

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.

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

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

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

2. 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

3. Start the services:

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

# View logs
docker-compose logs -f

# Stop services
docker-compose down

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.

1. Create a new Slack app at https://api.slack.com/apps
2. Enable Socket Mode and generate an app-level token
3. Check Allow users to send Slash commands and messages from the chat tab in App Home page, to enable direct message to Slack app.
4. Add the following Bot Token Scopes:
   • app_mentions:read
   • chat:write
   • im:history
   • im:read
   • im:write
   • users:read
   • users.profile:read
   • channels:history
   • groups:history
   • mpim:history
5. Enable Event Subscriptions and subscribe to:
   • app_mention
   • message.im
6. 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.

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

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

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

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-4.1"  # or gpt-4o, o3-pro

# Use Anthropic
export LLM_PROVIDER="anthropic"
export ANTHROPIC_API_KEY="your-anthropic-api-key"
export ANTHROPIC_MODEL="claude-sonnet-4.5"  # or claude-opus-4.1

# Or use Ollama
export LLM_PROVIDER="ollama"
export LANGCHAIN_OLLAMA_URL="http://localhost:11434"
export LANGCHAIN_OLLAMA_MODEL="llama3.3"  # or qwen2.5-coder, mistral-small-3, deepseek-r1

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

The client uses two main configuration approaches:

Configure LLM providers and Slack integration using environment variables:

The client includes comprehensive monitoring capabilities with both metrics and distributed tracing:

• Metrics Endpoint: Accessible at /metrics on the configured port
• Default Port: 8080 (configurable via --metrics-port flag)
• Metrics Available:
  • slackmcp_tool_invocations_total: Counter for tool invocations with labels for tool name, server, and error status
  • slackmcp_llm_tokens: Histogram for LLM token usage by type and model

• Supported Providers:
  • simple-otel: Basic OpenTelemetry tracing to OTLP endpoints (requires endpoint configuration)
  • langfuse-otel: Advanced LLM observability with Langfuse integration (requires endpoint and auth)
  • disabled: No tracing (default when no endpoint configured)
• Automatic Fallbacks: Failed providers automatically fall back to disabled state
• Comprehensive Tracking: Spans for LLM operations, tool calls, and user interactions with detailed attributes

Example configuration and usage:

# Access metrics endpoint
curl http://localhost:8080/metrics

# Run with custom metrics port
slack-mcp-client --metrics-port 9090

# Enable Langfuse tracing (example)
export LANGFUSE_ENDPOINT="https://cloud.langfuse.com"
export LANGFUSE_PUBLIC_KEY="pk-your-public-key"  
export LANGFUSE_SECRET_KEY="sk-your-secret-key"

# Enable simple OTLP tracing (example)
export OTEL_EXPORTER_OTLP_ENDPOINT="http://localhost:4318"

All configuration is now managed through a single config.json file with comprehensive options:

{
  "$schema": "https://github.com/tuannvm/slack-mcp-client/schema/config-schema.json",
  "version": "2.0",
  "slack": {
    "botToken": "${SLACK_BOT_TOKEN}",
    "appToken": "${SLACK_APP_TOKEN}",
    "messageHistory": 50,
    "thinkingMessage": "Processing..."
  },
  "llm": {
    "provider": "openai",
    "useNativeTools": true,
    "useAgent": false,
    "customPrompt": "You are a helpful assistant.",
    "maxAgentIterations": 20,
    "providers": {
      "openai": {
        "model": "gpt-4o",
        "apiKey": "${OPENAI_API_KEY}",
        "temperature": 0.7,
        "maxTokens": 2000
      },
      "anthropic": {
        "model": "claude-sonnet-4.5",
        "apiKey": "${ANTHROPIC_API_KEY}",
        "temperature": 0.7
      }
    }
  },
  "mcpServers": {
    "filesystem": {
      "command": "npx",
      "args": ["-y", "@modelcontextprotocol/server-filesystem", "/workspace"],
      "initializeTimeoutSeconds": 30,
      "tools": {
        "allowList": ["read_file", "write_file", "list_directory"],
        "blockList": ["delete_file"]
      }
    },
    "web-api": {
      "url": "http://localhost:8080/mcp",
      "transport": "sse",
      "initializeTimeoutSeconds": 30
    }
  },
  "rag": {
    "enabled": true,
    "provider": "openai",
    "chunkSize": 1000,
    "providers": {
      "openai": {
        "vectorStoreId": "vs_existing_store_id",
        "dimensions": 1536,
        "maxResults": 10
      }
    }
  },
  "timeouts": {
    "httpRequestTimeout": "30s",
    "toolProcessingTimeout": "3m",
    "mcpInitTimeout": "30s"
  },
  "retry": {
    "maxAttempts": 3,
    "baseBackoff": "500ms",
    "maxBackoff": "5s"
  },
  "monitoring": {
    "enabled": true,
    "metricsPort": 8080,
    "loggingLevel": "info"
  },
  "observability": {
    "enabled": true,
    "provider": "langfuse-otel",
    "endpoint": "${LANGFUSE_ENDPOINT}",
    "publicKey": "${LANGFUSE_PUBLIC_KEY}",
    "secretKey": "${LANGFUSE_SECRET_KEY}",
    "serviceName": "slack-mcp-client",
    "serviceVersion": "1.0.0"
  }
}

For detailed configuration options and migration guides, see the Configuration Guide.

The client supports optional automatic reloading to handle MCP server restarts without downtime - perfect for Kubernetes deployments where MCP servers may restart independently.

Note: The reload feature is disabled by default and must be explicitly enabled in your configuration file.

To enable reload functionality, add reload settings to your config.json:

{
  "version": "2.0",
  "reload": {
    "enabled": true,
    "interval": "30m"
  }
}

Configuration Options:

• enabled: Must be set to true to activate reload functionality (default: false)
• interval: Time between automatic reloads (default: "30m", minimum: "10s")

Automatic Reload: When enabled, the application automatically reloads at the configured interval to reconnect to MCP servers and refresh tool discovery.

Manual Reload: Even with automatic reload disabled, you can trigger manual reloads using signals:

# In Kubernetes
kubectl exec -it <pod-name> -- kill -USR1 1

# Local process
kill -USR1 <process-id>

• Zero Downtime: Application stays running during reload
• Kubernetes-Friendly: Pod continues running while application components restart
• Opt-in: Disabled by default, only enabled when explicitly configured
• Flexible: Both automatic (periodic) and manual (signal) triggers
• Safe: Minimum interval validation prevents excessive reloading

When enabled, the reload feature automatically:

• Reconnects to all configured MCP servers
• Rediscovers available tools
• Refreshes configuration settings
• Maintains Slack connection throughout the process

Perfect for production environments where MCP servers may restart due to updates, scaling, or maintenance.

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.

The client supports three transport modes:

• SSE (default): Uses Server-Sent Events for real-time communication with the MCP server, includes automatic retry logic for enhanced reliability
• HTTP: Uses HTTP POST requests with JSON-RPC for communication
• stdio: Uses standard input/output for local development and testing

Comprehensive documentation is available in the docs/ directory:

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

• 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

• 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

• Setup: Start with the Slack Configuration Guide for initial setup
• Agent Mode: See the Agent Mode section above for autonomous AI agents with tool chaining
• RAG: Check the RAG Implementation Guide for document knowledge base integration
• Formatting: See the Slack Formatting Guide for message formatting capabilities
• RAG SQLite: See the RAG SQLite Implementation for native Go implementation with modern upload UX
• Development: Check the Implementation Notes for technical details
• Testing: Use the Testing Guide for testing procedures and debugging
• Monitoring: See the metrics configuration section above for Prometheus integration
• Dependencies: Review Dependencies for version tracking and upgrade history

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

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

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

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

• 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

• 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

• Unit Tests: Running tests with race detection and code coverage reporting
• Build Verification: Ensuring the codebase builds successfully

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