Which integrations are available for this server?

Supports running the MCP server in a Docker container as an alternative containerization option. Supports running the MCP server in a Podman container, with configuration examples provided for containerized deployment. Referenced as a method for extracting Slack authentication tokens through automation. Provides comprehensive Slack integration with features for channel management, messaging, commands, reactions, and authentication, enabling AI agents to interact with Slack, monitor logs, post alerts, and facilitate team collaboration.

How do I use Slack-MCP?

1. Click on "Install Server". 2. Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state. 3. In the chat, type @ followed by the MCP server name and your instructions, e.g., "@Slack-MCP check the pipeline logs for errors and alert the team in #dev-alerts" That's it! The server will respond to your query, and you can continue using it as needed. Here is a step-by-step guide with screenshots.

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

Related MCP server: Slack MCP Server

🏗️ 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

User: "Check recent pipeline logs and alert team if errors found"
Pipeline-Toolkit AI: Understands intent, orchestrates actions
Slack-MCP-Logger:
- Reads logs via get_server_logs()
- Posts alerts via post_message()
- Adds reactions via add_reaction()
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

Slack-MCP