slack-mcp

MCP server for Slack with log monitoring capabilities

🚀 Features

Core Slack Integration

• Channel Management: Join channels and retrieve message history
• Messaging: Post messages and threaded replies
• Commands: Execute Slack slash commands
• Reactions: Add emoji reactions to messages
• Authentication: Verify identity and permissions

Log Monitoring & Team Collaboration

• Server Log Reading: Access logs from any MCP server or application
• Automated Alerts: Post log analysis to Slack channels
• Team Debugging: Share logs instantly with team members
• Multi-Server Monitoring: Monitor logs from multiple services

🏗️ Architecture

┌─────────────────────┐    ┌─────────────────────┐    ┌─────────────────────┐
│    User/Team        │───▶│   Pipeline-Toolkit  │───▶│   Slack-MCP-Logger  │
│  (Natural Language) │    │   (AI Orchestrator) │    │   (Slack Specialist)│
└─────────────────────┘    └─────────────────────┘    └─────────────────────┘
                                      ▲                          │
                                      │                          ▼
                           ┌─────────────────────┐    ┌─────────────────────┐
                           │   Log Files         │    │    Slack API        │
                           │ • pipeline_bot.log  │    │ • Channels          │
                           │ • testing_farm.log  │    │ • Messages          │
                           │ • application.log   │    │ • Reactions         │
                           │ • error.log         │    │ • Commands          │
                           └─────────────────────┘    └─────────────────────┘

Workflow Example

1. User: "Check recent pipeline logs and alert team if errors found"
2. Pipeline-Toolkit AI: Understands intent, orchestrates actions
3. Slack-MCP-Logger:
   • Reads logs via get_server_logs()
   • Posts alerts via post_message()
   • Adds reactions via add_reaction()
4. Result: Team gets notified in Slack with actionable log data

🛠️ Built-in Tools

1. Channel Management

get_channel_history(channel_id: str) -> list[dict]
join_channel(channel_id: str, skip_log: bool = False) -> bool

2. Messaging

post_message(channel_id: str, message: str, thread_ts: str = "", skip_log: bool = False) -> bool
post_command(channel_id: str, command: str, text: str, skip_log: bool = False) -> bool

3. Reactions & Interactions

add_reaction(channel_id: str, message_ts: str, reaction: str) -> bool

4. Authentication

whoami() -> str

5. 🆕 Log Monitoring

get_server_logs(log_file_path: str, lines: int = 50) -> str

📋 Usage Examples

Log Monitoring & Team Alerts

# Read recent pipeline logs
logs = get_server_logs("logs/pipeline_bot.log", 100)

# Alert team with logs
post_message("C-DEV-TEAM", f"🚨 Pipeline Issues:\n{logs}")

# Mark for urgent attention  
add_reaction("C-DEV-TEAM", message_ts, "rotating_light")

Automated Monitoring Workflow

# Check multiple log sources
pipeline_logs = get_server_logs("logs/pipeline_bot.log", 50)
error_logs = get_server_logs("/var/log/app/error.log", 30)

# Post to different channels based on content
if "ERROR" in pipeline_logs:
    post_message("C-ALERTS", f"⚠️ Pipeline errors:\n{pipeline_logs}")
    
if "CRITICAL" in error_logs:
    post_message("C-INCIDENTS", f"🚨 Critical app errors:\n{error_logs}")

Daily Team Updates

# Morning standup automation
daily_logs = get_server_logs("logs/pipeline_bot.log", 200)
post_message("C-STANDUP", f"☀️ Overnight activity summary:\n{daily_logs}")

🚀 Running with Podman or Docker

You can run the slack-mcp server in a container using Podman or Docker:

Example configuration for running with Podman:

{
  "mcpServers": {
    "slack": {
      "command": "podman",
      "args": [
        "run",
        "-i",
        "--rm",
        "-e", "SLACK_XOXC_TOKEN",
        "-e", "SLACK_XOXD_TOKEN",
        "-e", "MCP_TRANSPORT",
        "-e", "LOGS_CHANNEL_ID",
        "quay.io/redhat-ai-tools/slack-mcp"
      ],
      "env": {
        "SLACK_XOXC_TOKEN": "xoxc-...",
        "SLACK_XOXD_TOKEN": "xoxd-...",
        "MCP_TRANSPORT": "stdio",
        "LOGS_CHANNEL_ID": "C7000000",
      }
    }
  }
}

🌐 Running with non-stdio transport

To run the server with a non-stdio transport (such as SSE), set the MCP_TRANSPORT environment variable to a value other than stdio (e.g., sse).

Example configuration to connect to a non-stdio MCP server:

{
  "mcpServers": {
    "slack": {
      "url": "https://slack-mcp.example.com/sse",
      "headers": {
        "X-Slack-Web-Token": "xoxc-...",
        "X-Slack-Cookie-Token": "xoxd-..."
      }
    }
  }
}

🔐 Authentication

Extract your Slack XOXC and XOXD tokens easily using browser extensions or Selenium automation: https://github.com/maorfr/slack-token-extractor.

🔧 Environment Variables

• SLACK_XOXC_TOKEN: Slack web token (required)
• SLACK_XOXD_TOKEN: Slack cookie token (required)
• MCP_TRANSPORT: Transport mode (stdio or sse, default: stdio)
• LOGS_CHANNEL_ID: Channel ID for logging MCP operations (required)
• LOG_BASE_PATH: Optional base path for log file access security

🤝 Integration with Pipeline-Toolkit

This MCP server works seamlessly with Pipeline-Toolkit to provide:

• Natural Language Processing: Ask questions in plain English
• Intelligent Tool Selection: AI chooses the right tools automatically
• Multi-Server Coordination: Monitor logs from multiple MCP servers
• Team Collaboration: Share insights and alerts through Slack

Example Integration

# In Pipeline-Toolkit
"Check recent logs and update the team if there are any errors"

# Results in:
# 1. get_server_logs("logs/pipeline_bot.log", 100)
# 2. AI analysis of log content
# 3. post_message("C-TEAM", error_summary) if issues found
# 4. add_reaction() for team attention

📊 Use Cases

• DevOps Monitoring: Automated log analysis and team alerts
• Incident Response: Quick log sharing and team coordination
• Daily Standups: Automated activity summaries
• Build Monitoring: Pipeline status updates and failure alerts
• Multi-Service Debugging: Centralized log access across services