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pluggedin-app

The Crossroads for AI Data Exchanges. A unified, self-hostable web interface for discovering, configuring, and managing Model Context Protocol (MCP) servers—bringing together AI tools, workspaces, prompts, and logs from multiple MCP sources (Claude, Cursor, etc.) under one roof.

Installation
Run this command in your terminal to add the MCP server to Claude Code.
Run in terminal:
Command
claude mcp add --transport stdio veriteknik-pluggedin-app docker run -i veriteknik/pluggedin:latest \
  --env PORT="Host port for MCP server (default 12005)" \
  --env MCP_API_KEY="optional API key for MCP access"

How to use

plugged.in is an all-in-one AI-CMS and MCP-enabled platform that emphasizes persistence, versioning, and interoperability. It embeds a robust MCP hub within a single web application, enabling you to connect 1,500+ MCP-compatible tools, filter and manage integrations, and use an in-process RAG engine for document indexing and semantic search. With this MCP server, you can discover, configure, and monitor external MCP servers, test model interactions, and run multiple tools in a unified interface. The system also supports live debugging, per-server configuration, and OAuth-based authentication to securely orchestrate tools across your workspace.

How to install

Prerequisites:

  • Docker and Docker Compose installed on your system
  • Sufficient disk space for a Postgres database, vector storage, and application assets

Step-by-step:

  1. Install Docker
  2. Pull and run the plugged.in image via Docker (example)
# Pull and run the latest Plugged.in image (MCP-enabled)
docker run -d --name pluggedin-app -p 12005:12005 veriteknik/pluggedin:latest
  1. Initialize environment and data (optional)
    • If your image requires a data directory or environment configuration, mount volumes and supply environment variables as needed.
# Example: with docker-compose (recommended for multi-service setup)
# Create a docker-compose.yml that defines the services (web, postgres, etc.)
# Then:
docker compose up -d --build
  1. Access the MCP-enabled interface

  2. Verify MCP connectivity

    • Use the MCP hub UI to discover and configure external MCP servers, test tool interactions, and validate OAuth flows.

Additional notes

Tips and notes:

  • This setup uses a multi-arch Docker image; ensure your host supports amd64 or arm64. The image includes PostgreSQL with pgvector and an embedded in-process RAG engine.
  • If upgrading from older versions, prefer a fresh start or follow the provided migration guidance in the repository notes.
  • For security, enable OAuth 2.1 where supported and consider configuring TLS/HTTPS in a reverse-proxy setup for production.
  • Monitor MCP activity via the built-in auditing and analytics features to detect misconfigurations or tool conflicts.
  • The MCP cache and vector data are stored in persistent volumes; back them up regularly if you value long-term data retention.

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