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2025-03-07

An MCP server that any process can pipe stdout logs to including console logs from nodejs programs

3 years

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stdout-mcp-server

A Model Context Protocol (MCP) server that captures and manages stdout logs through a named pipe system. This server is particularly useful for:

  • Capturing logs from multiple processes or applications and making them available for debugging in Cursor IDE.
  • Monitoring application output in real-time and providing a MCP interface to query, filter, and analyze logs

How It Works

  1. The server creates a named pipe at a specific location (/tmp/stdout_pipe on Unix/MacOS or \\.\pipe\stdout_pipe on Windows)

  2. Any application can write logs to this pipe using standard output redirection. For example:

your_application | tee /tmp/stdout_pipe # or
your_application > /tmp/stdout_pipe
  1. The server monitors the pipe, captures all incoming logs, and maintains a history of the last 100 entries

  2. Through MCP tools, you can query, filter, and analyze these logs

System Requirements

Before installing, please ensure you have:

  • Node.js v18 or newer

Installation Options

Option 1: Installation in Cursor

  1. Open Cursor and navigate to Cursor > Settings > MCP Servers
  2. Click on "Add new MCP Server"
  3. Update your MCP settings file with the following configuration:
name: stdout-mcp-server
type: command
command: npx stdout-mcp-server

Option 2: Installation in other MCP clients

Installation in other MCP clients

For macOS/Linux:

{
  "mcpServers": {
    "stdio-mcp-server": {
      "command": "npx",
      "args": [
        "stdio-mcp-server"
      ]
    }
  }
}

For Windows:

{
  "mcpServers": {
    "mcp-installer": {
      "command": "cmd.exe",
      "args": ["/c", "npx", "stdio-mcp-server"]
    }
  }
}

Usage Examples

Redirecting Application Logs

To send your application's output to the pipe:

# Unix/MacOS
your_application > /tmp/stdout_pipe

# Windows (PowerShell)
your_application > \\.\pipe\stdout_pipe

Monitoring Multiple Applications

You can redirect logs from multiple sources:

# Application 1
app1 > /tmp/stdout_pipe &

# Application 2
app2 > /tmp/stdout_pipe &

Querying Logs

Your AI will use the get-logs tool in your MCP client to retrieve and filter logs:

// Get last 50 logs
get-logs()

// Get last 100 logs containing "error"
get-logs({ lines: 100, filter: "error" })

// Get logs since a specific timestamp
get-logs({ since: 1648675200000 }) // Unix timestamp in milliseconds

Features

  • Named pipe creation and monitoring
  • Real-time log capture and storage
  • Log filtering and retrieval through MCP tools
  • Configurable log history (default: 100 entries)
  • Cross-platform support (Windows and Unix-based systems)

Named Pipe Locations

  • Windows: \\.\pipe\stdout_pipe
  • Unix/MacOS: /tmp/stdout_pipe

Available Tools

get-logs

Retrieve logs from the named pipe with optional filtering:

Parameters:

  • lines (optional, default: 50): Number of log lines to return
  • filter (optional): Text to filter logs by
  • since (optional): Timestamp to get logs after

Example responses:

// Response format
{
  content: [{
    type: "text",
    text: "[2024-03-20T10:15:30.123Z] Application started\n[2024-03-20T10:15:31.456Z] Connected to database"
  }]
}

License

MIT License

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    Reviews

    2 (1)
    Avatar
    user_2KSuCwmB
    2025-04-15

    As a dedicated user of the ???? Kubernetes AI Management System, I've found it incredibly efficient for managing and deploying AI workloads on Kubernetes. The system optimizes resource utilization and simplifies complex tasks seamlessly. Kudos to hariohmprasath for creating such an innovative solution! Highly recommended for AI-driven projects. Check it out at https://mcp.so/server/k8s-ai/hariohmprasath.