Click on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type @ followed by the MCP server name and your instructions, e.g., "@QMCP MultiRoom MCP Server create a room, add a blackboard, and run a Bell-state circuit"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
A multi-room collaborative MCP server with quantum-circuit simulation, tasks, rooms, messages, blackboards, documents, plugin registration, visualizations, search, OAuth helpers, and collaborative model-training tools.
The server implementation is preserved as provided in qmcp-server.py. The additional deployment files are only for packaging and running it on MCPize or another container-based MCP host.
Import this GitHub repository in MCPize.
Choose the direct deployment option.
MCPize will build the included Dockerfile and start the server over STDIO.
The default container command is:
python qmcp-server.pyRelated MCP server: ATMcp
Python 3.11 or newer is recommended.
python -m venv .venv
. .venv/bin/activate
pip install -r requirements.txt
python qmcp-server.pyThe server defaults to the STDIO transport, which is the expected mode for MCPize's GitHub deployment flow.
The original implementation also supports the existing command-line options and environment variables:
MCP_TRANSPORT=streamable-http MCP_HOST=0.0.0.0 MCP_PORT=8000 python qmcp-server.pyWhen a non-STDIO transport is selected, the original code starts its dashboard/OAuth web server on WEB_PORT or MCP_PORT + 1.
Runtime state is held in memory, matching the supplied implementation.
The models/ directory is created by the server when model files are used; it is intentionally not pre-populated.
No API keys or credentials are required by the repository.
This project is provided as-is; review the server's authentication and persistence model before exposing an HTTP endpoint publicly.
Hosted runtime for persistent agent teams, durable workflows, memory, schedules, and goals.
Coordination hub for AI coding agents: message teammates, ask humans, audit every event.
Agent-native collaboration network: orchestrate a team of long-running agents from any MCP client.
The back-office workspace for your team's AIs: tasks, knowledge and context shared over MCP.
