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
- 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()
- Reads logs via
- 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 (stdioorsse, 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