About stdout-mcp-server
stdout-mcp-server captures stdout from multiple processes through named pipes and makes logs queryable. It is for developers debugging applications in Cursor and other MCP clients.
Key features
- 01Named pipe creation and monitoring
- 02Real-time log capture and storage
- 03Filter logs by text or time
- 04Keep the latest 100 entries
Use cases
- 01Debugging multiple application processes
- 02Viewing logs after a timestamp
MCP support
The MCP server exposes a get-logs tool for retrieving logs and filtering them by line count, text, or timestamp.
Configuration
Local server config is the same for every client. Copy it into your client’s MCP config file.
Tools
1Saved tool list · updated 3w ago
| Parameter | Type | Required | Description |
|---|---|---|---|
| lines | number | No | Number of log lines to return |
| filter | string | No | Text to filter logs by |
| since | number | No | Timestamp to get logs after |
Details
From the project READMEstdout-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
-
The server creates a named pipe at a specific location (
/tmp/stdout_pipeon Unix/MacOS or\\.\pipe\stdout_pipeon Windows) -
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
-
The server monitors the pipe, captures all incoming logs, and maintains a history of the last 100 entries
-
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
- Open Cursor and navigate to
Cursor > Settings > MCP Servers - Click on "Add new MCP Server"
- 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 returnfilter(optional): Text to filter logs bysince(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
Comments
New to MCP?
From what the protocol is to installing your first server, these answers cover most first questions.
An open protocol that defines how AI applications connect to external tools and data in a uniform way. Clients talk to the model; MCP servers provide tools, resources and prompt templates. They exchange standard messages, so one server can be reused by many clients.