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Slack-MCP

by caramuto-redhat
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Python
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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

This server cannot be installed

-
security - not tested
F
license - not found
-
quality - not tested

How are these scores calculated?

A Message Control Protocol server that integrates with Slack to provide channel management, messaging capabilities, and log monitoring across multiple servers, enabling teams to share logs and receive automated alerts in Slack channels.

  1. 🚀 Features
    1. Core Slack Integration
    2. Log Monitoring & Team Collaboration
  2. 🏗️ Architecture
    1. Workflow Example
  3. 🛠️ Built-in Tools
    1. 1. Channel Management
    2. 2. Messaging
    3. 3. Reactions & Interactions
    4. 4. Authentication
    5. 5. 🆕 Log Monitoring
  4. 📋 Usage Examples
    1. Log Monitoring & Team Alerts
    2. Automated Monitoring Workflow
    3. Daily Team Updates
  5. 🚀 Running with Podman or Docker
    1. 🌐 Running with non-stdio transport
      1. 🔐 Authentication
        1. 🔧 Environment Variables
          1. 🤝 Integration with Pipeline-Toolkit
            1. Example Integration
          2. 📊 Use Cases

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