VeriTeknik/pluggedin-mcp

> [!IMPORTANT]
> **Project status (October 2026): open source, community-driven.**
> plugged.in, the hosted service, is moving to a new, separately developed platform. After that move,
> this repository will no longer run plugged.in. The code stays open source under its current license, and the
> community is welcome to carry it on: issues, pull requests, forks and new maintainers are welcome, and
> self-hosting remains supported. The cutover date will be announced here. At the cutover, the current service
> moves to v1.plugged.in and keeps running there until December 31, 2026.
> What this means
>
> This client talks to the plugged.in API. Before the cutover we will release a version that points it to
> v1.plugged.in. After December 31, 2026, point it at your own pluggedin-app instance.
The Crossroads for AI Data Exchanges
A unified MCP hub that gives your AI Knowledge , Memory , and Tools — not just a proxy. Manage and test all MCP servers from a single connection while powering document-aware and memory-augmented workflows across clients.
📋 Overview
The plugged.in MCP Proxy Server is a powerful middleware that aggregates multiple Model Context Protocol (MCP) servers into a single unified interface. It fetches tool, prompt, and resource configurations from the plugged.in App and intelligently routes requests to the appropriate underlying MCP servers.
This proxy enables seamless integration with any MCP client (Claude, Cline, Cursor, etc.) while providing advanced management capabilities through the plugged.in ecosystem.
Hub Pillars: Knowledge · Memory · Tools · Proxy
**Knowledge (RAG v2 / AI Document Exchange)**
Search and ground model outputs with unified, attribution‑aware document retrieval. MCP servers can create and manage documents in your library with versioning, visibility controls, and model attribution. Use the built‑in RAG to search across all connected sources and return relevant snippets and metadata.
**Memory (Persistent AI Memory)**
Long‑lived, workspace/profile‑scoped memory that survives sessions. The hub integrates with the plugged.in App's persistent memory so agent actions and insights can be stored and recalled across tasks. Built‑in memory tools are on the roadmap to expose low‑friction get/put/search patterns under the same auth model.
**Tools**
Aggregate built‑in capabilities with downstream MCP servers (STDIO, SSE, Streamable HTTP). Tool discovery is cached and can be refreshed on demand; hub‑level discovery returns a unified catalog for any MCP client. The hub supports tools, resources, resource templates, and prompts.
**Proxy**
One connection for every client. Run as STDIO (default) or Streamable HTTP with optional API auth and stateless mode. Works with Claude Desktop, Cline, Cursor, MCP Inspector, and more; keep your existing client configs while centralizing policies and telemetry.
> ⭐ **If you find this project useful, please consider giving it a star on GitHub!** It helps us reach more developers and motivates us to keep improving.
✨ Key Features
🚀 Core Capabilities
- **Built-in AI Playground**: Test your MCPs instantly with Claude, Gemini, OpenAI, and xAI without any client setup
- **Universal MCP Compatibility**: Works with any MCP client including Claude Desktop, Cline, and Cursor
- **Multi-Server Support**: Connect to STDIO, SSE, and Streamable HTTP MCP servers
- **Dual Transport Modes**: Run proxy as STDIO (default) or Streamable HTTP server
- **Unified Document Search**: Search across all connected servers with built-in RAG capabilities
- **AI Document Exchange (RAG v2)**: MCP servers can create and manage documents in your library with full attribution
- **Notifications from Any Model**: Receive real-time notifications with optional email delivery
- **Multi-Workspace Layer**: Switch between different sets of MCP configurations with one click
- **API-Driven Proxy**: Fetches capabilities from plugged.in App APIs rather than direct discovery
- **Full MCP Support**: Handles tools, resources, resource templates, and prompts
- **Custom Instructions**: Supports server-specific instructions formatted as MCP prompts
🎯 New in v1.5.0 (RAG v2 - AI Document Exchange)
- **AI Document Creation**: MCP servers can now create documents directly in your library
- Full model attribution tracking (which AI created/updated the document)
- Version history with change tracking
- Content deduplication via SHA-256 hashing
- Support for multiple formats: MD, TXT, JSON, HTML, PDF, and more