Which integrations are available for this server?

Supports running the MCP server in a [Docker](/mcp/servers/integrations/docker) container as an alternative containerization option. Supports running the MCP server in a [Podman](/mcp/servers/integrations/podman) container, with configuration examples provided for containerized deployment. Referenced as a method for extracting Slack authentication tokens through automation. Provides comprehensive [Slack](/mcp/servers/integrations/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.

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