MCP server
by amitdeshmukh
StdoutMCP is a lightweight server for capturing and managing stdout logs from multiple processes, with powerful querying
Captures stdout logs from multiple processes through a named pipe and provides querying/filtering tools for debugging and monitoring.
StdoutMCP is a community-built MCP server published by amitdeshmukh that provides AI assistants with tools and capabilities via the Model Context Protocol. StdoutMCP is a lightweight server for capturing and managing stdout logs from multiple processes, with powerful querying It is categorized under developer tools.
You can install StdoutMCP in your AI client of choice. Use the install panel on this page to get one-click setup for Cursor, Claude Desktop, VS Code, and other MCP-compatible clients. This server runs locally on your machine via the stdio transport.
MIT
StdoutMCP is released under the MIT license. This is a permissive open-source license, meaning you can freely use, modify, and distribute the software.
Add new capabilities to Claude beyond text generation
Example
Access external data sources, execute code, interact with tools and services
Transform Claude from chatbot to action-taking agent
Provide Claude with access to relevant context and data
Example
Load project documentation, access knowledge bases, query databases
Get more accurate, context-aware responses
Automate multi-step workflows combining AI and external tools
Example
Research → Summarize → Create document → Send notification
Complete complex tasks end-to-end without manual steps
Share your MCP server with the developer community
We wired StdoutMCP into a staging workspace; the listing’s GitHub and npm pointers saved time versus hunting across READMEs.
According to our notes, StdoutMCP benefits from clear Model Context Protocol framing — fewer ambiguous “AI plugin” claims.
Useful MCP listing: StdoutMCP is the kind of server we cite when onboarding engineers to host + tool permissions.
According to our notes, StdoutMCP benefits from clear Model Context Protocol framing — fewer ambiguous “AI plugin” claims.
We wired StdoutMCP into a staging workspace; the listing’s GitHub and npm pointers saved time versus hunting across READMEs.
Strong directory entry: StdoutMCP surfaces stars and publisher context so we could sanity-check maintenance before adopting.
StdoutMCP has been reliable for tool-calling workflows; the MCP profile page is a good permalink for internal docs.
StdoutMCP is a well-scoped MCP server in the explainx.ai directory — install snippets and categories matched our Claude Code setup.
I recommend StdoutMCP for teams standardizing on MCP; the explainx.ai page compares cleanly with sibling servers.
Strong directory entry: StdoutMCP surfaces stars and publisher context so we could sanity-check maintenance before adopting.
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A Model Context Protocol (MCP) server that captures and manages stdout logs through a named pipe system. This server is particularly useful for:
The server creates a named pipe at a specific location (/tmp/stdout_pipe on Unix/MacOS or \.\pipe\stdout_pipe on 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
Before installing, please ensure you have:
Cursor > Settings > MCP Serversname: stdout-mcp-server
type: command
command: npx stdout-mcp-server
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"]
}
}
}
To send your application's output to the pipe:
# Unix/MacOS
your_application > /tmp/stdout_pipe
# Windows (PowerShell)
your_application > \.\pipe\stdout_pipe
You can redirect logs from multiple sources:
# Application 1
app1 > /tmp/stdout_pipe &
# Application 2
app2 > /tmp/stdout_pipe &
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
\.\pipe\stdout_pipe/tmp/stdout_pipeRetrieve 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 afterExample responses:
// Response format
{
content: [{
type: "text",
text: "[2024-03-20T10:15:30.123Z] Application started
[2024-03-20T10:15:31.456Z] Connected to database"
}]
}
MIT License
Prerequisites
Time Estimate
15-60 minutes depending on server complexity
Steps
Troubleshooting
✓ Do
✗ Don't
💡 Pro Tips
Architecture
Model Context Protocol standardizes how AI hosts (Claude, Cursor) communicate with external tools and data sources through server implementations.
Protocols
Compatibility
✓ Use when
Use when you need Claude to access external data, execute actions, or integrate with tools. Best for extending AI capabilities beyond conversation.
✗ Avoid when
Avoid when native integrations exist (use official APIs directly), for real-time critical systems, or when security/compliance requires zero external dependencies.