Pluggedin App
The plugged.in App is a comprehensive web application for managing Model Context Protocol (MCP) servers, providing a unified interface to discover, configure, and use AI tools across multiple MCP servers. It supports multi - workspaces, an interactive testing platform, tool management, resource discovery, etc., can be integrated with various MCP clients, and supports self - hosted deployment.
rating : 2.5 points
downloads : 25
What is plugged.in?
plugged.in is a web application for managing Model Context Protocol (MCP) servers. It provides an intuitive interface to discover, configure, and use AI tools across multiple MCP servers.How to use plugged.in?
Through simple Docker installation or direct deployment, you can quickly set up your own plugged.in instance. Then integrate and use it with various MCP clients (such as Claude, Cursor, etc.) through the web interface or API.Use cases
Suitable for developers and teams who need to centrally manage multiple AI tools, especially in scenarios where resources and contexts need to be shared across different MCP servers.Main features
Multi - workspace supportSwitch between different MCP configuration sets to avoid context pollution
Interactive playgroundTest and experiment with your MCP tools directly in the browser
Tool managementDiscover, organize, and manage AI tools from multiple sources
Resource discoveryView available resources and templates of connected MCP servers
Custom instructionsAdd server - specific instructions, which can be used as MCP prompts
Advantages and limitations
Advantages
Manage multiple MCP servers with a unified interface
Support multiple popular MCP clients
Provide detailed interaction log records
Self - hosted, with full control of data
Open - source and extensible
Limitations
Basic server management knowledge is required for self - hosting
Some advanced features require MCP proxy configuration
Extra resource planning is required for large - scale deployment
How to use
Installation and deployment
Quickly deploy via Docker or install manually
Configure MCP clients
Configure the plugged.in proxy in your favorite MCP client
Add MCP servers
Add the MCP servers you want to manage in the web interface
Start using
Call your AI tools through the interface or API
Usage examples
Cross - team AI tool sharingDevelopment teams use plugged.in to centrally manage AI tools developed by various departments for cross - team sharing
Multi - environment configuration managementConfigure different MCP server sets for development, testing, and production environments and switch easily
AI tool experimentTest newly developed AI tools in the interactive playground without modifying client configurations
Frequently Asked Questions
What's the difference between using plugged.in and directly using MCP servers?
Do I need programming knowledge to use plugged.in?
Which MCP clients does plugged.in support?
How is the data stored and protected?
How to add a new MCP server?
Related resources
plugged.in MCP Proxy repository
The MCP proxy component of plugged.in
Model Context Protocol specification
The official MCP specification document
Claude Desktop documentation
The usage guide for the Claude client
Cursor documentation
The documentation for the Cursor AI assistant
Docker installation guide
The official Docker installation document
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