plugged.in MCP Hub — Proxy · Knowledge · Memory · Tools Details
The plugged.in MCP Proxy Server operates as a central hub that aggregates multiple Model Context Protocol (MCP) servers into a single, unified interface. It orchestrates knowledge, memory, and tools across connected MCPs, enabling clients to query documents, manage memory, and invoke tools from various servers through one connection. With support for STDIO, Server-Sent Events (SSE), and Streamable HTTP transports, it enables seamless integration with popular MCP clients like Claude Desktop, Cline, and Cursor while providing policy, telemetry, and registry features for scalable deployments.
This proxy fetches tool, prompt, and resource configurations from the plugged.in App APIs and exposes a unified catalog of capabilities. It supports static built-in tools, memory clipboard operations, and dynamic tools discovered from connected MCP servers, including tool discovery, RAG-based search, document management, and notifications. The hub also offers configuration options for HTTP transport, authentication, and session management, making it possible to run as a stateless HTTP service or a stateful STDIO proxy, with optional API-key protection for HTTP endpoints.
Use Case
The plugged.in MCP Hub provides a single entry point to manage and query multiple MCP servers. It centralizes tool discovery, document management, and memory operations, making it easier to build AI workflows that span many data sources.
Example usage:
npx -y @pluggedin/pluggedin-mcp-proxy@latest --pluggedin-api-key YOUR_API_KEY{
"mcpServers": {
"pluggedin": {
"command": "npx",
"args": ["-y", "@pluggedin/pluggedin-mcp-proxy@latest"],
"env": {
"PLUGGEDIN_API_KEY": "YOUR_API_KEY"
}
}
}
}npx -y @pluggedin/pluggedin-mcp-proxy@latest --pluggedin-api-key YOUR_API_KEYnpx -y @pluggedin/pluggedin-mcp-proxy@latest --transport streamable-http --pluggedin-api-key YOUR_API_KEYAvailable Tools (17)
Examples & Tutorials
Real usage patterns directly from the docs:
npx -y @pluggedin/pluggedin-mcp-proxy@latest --pluggedin-api-key YOUR_API_KEY{
"mcpServers": {
"pluggedin": {
"command": "npx",
"args": ["-y", "@pluggedin/pluggedin-mcp-proxy@latest"],
"env": {
"PLUGGEDIN_API_KEY": "YOUR_API_KEY"
}
}
}
}npx -y @pluggedin/pluggedin-mcp-proxy@latest --pluggedin-api-key YOUR_API_KEY<h1 class="text-2xl font-semibold mt-5 mb-3">Run as HTTP server on default port (12006)</h1>
npx -y @pluggedin/pluggedin-mcp-proxy@latest --transport streamable-http --pluggedin-api-key YOUR_API_KEYInstallation Guide
Install and run with npx (latest v1.0.0):
npx -y @pluggedin/pluggedin-mcp-proxy@latest --pluggedin-api-key YOUR_API_KEYIntegration Guides
Frequently Asked Questions
Is this your MCP?
Claim ownership and get verified badge
Hub supports STDIO (default) or Streamable HTTP transport with optional API authentication. Streamable HTTP endpoints include POST /mcp for messages, GET /mcp for SSE, DELETE /mcp to terminate sessions, and GET /health for health checks. The project also emphasizes Smithery compatibility and Registry v2 features, with ongoing updates to protocol versions and health checks. Ensure Node.js engines and API keys align with the environment (e.g., Node >=18, API key required for HTTP endpoints).
Node.js 18+ (recommended v20+). An API key from the plugged.in App (get one at plugged.in/api-keys).
Compare Alternatives
Similar MCP Tools
6 related toolsAnki MCP Server
A Model Context Protocol (MCP) server that enables AI assistants to interact with Anki, the spaced repetition flashcard application. The Anki MCP Server allows AI models to access Anki's card data, enabling features like automated flashcard creation, review, and management.
1MCP Agent
A unified Model Context Protocol server implementation that aggregates multiple MCP servers into one. The 1mcp-app/agent is an open-source project that provides a single entry point for multiple MCP servers, making it easier to manage and interact with various AI models and tools.
Roundtable AI MCP Server
Roundtable AI MCP Server is a zero-configuration local MCP server that unifies multiple AI coding assistants (Codex, Claude Code, Cursor, Gemini) through intelligent auto-discovery and a standardized interface. It coordinates specialized sub-agents from within your IDE to solve engineering problems in parallel, sharing context and synthesizing responses into a single, high-quality output. This documentation details installation, available MCP tools, integration with popular IDEs, and a broad ecosystem of specialized tools and CLIs that can be invoked as part of a roundtable-powered workflow, enabling developers to delegate tasks to the right AI for each facet of a problem without leaving their development environment.
MCPJungle
MCPJungle is a self-hosted MCP Gateway and Registry for AI agents. It serves as a central registry and gateway to manage Model Context Protocol (MCP) servers and the tools they expose. By consolidating MCP server registration, tool discovery, and access control, MCPJungle enables AI agents and clients to discover, group, and securely invoke tools from a single, unified gateway. The project provides a CLI, Docker-based deployment options, and enterprise-ready features such as tool grouping, access control, and observability to streamline MCP-based workflows across organizations.
mcpmcp-server
mcpmcp-server is a focused solution for discovering, setting up, and integrating MCP servers with your favorite clients to unlock AI-powered workflows. It streamlines how you connect MCP-powered servers to popular clients, enabling seamless AI-assisted interactions across your daily tools. The project emphasizes an approachable, config-driven approach to linking MCP servers with clients like Claude Desktop, while directing you to the homepage for variations across apps and platforms. This README highlights a practical JSON configuration example and notes on supported environments, helping you get started quickly and confidently.
Imagen3-MCP
Imagen3-MCP is an image generation service based on Google's Imagen 3.0 that exposes its functionality through MCP (Model Control Protocol). The project provides a server to run a local MCP service that accesses Google Gemini-powered image generation, enabling developers to integrate advanced image synthesis into their applications. The documentation covers prerequisites (Gemini API key), installation steps for Cherry Studio, and a Cursor-based JSON configuration example for embedding the MCP server in broader tooling. This MCP is designed to be deployment-friendly, with configurable environment variables and optional proxy settings to adapt to various network environments.