MCP
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., "@MCPInspect the available local tools, then execute the one that gets the current time."
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.
Local MCP + Groq + OpenAI Agents SDK
This classroom example contains:
app.py: a persistent local Streamable HTTP MCP server with several tools.mcp_groq_agent.ipynb: an agent that discovers and calls those tools.
Setup
Use Python 3.10 or newer. In a terminal, enter this project folder, create a
virtual environment, and install everything needed by both the server and notebook
(replace python3.11 with your modern Python command if necessary):
cd /Users/somenathmandal/Documents/transfer/grok-playground/MCP
python3.11 -m venv .venv
source .venv/bin/activate
python -m pip install -r requirements.txtCreate .env from the example and put your Groq API key in it:
cp .env.example .envGROQ_API_KEY=replace_with_your_groq_key
GROQ_MODEL=openai/gpt-oss-20bNever commit or share .env; it is excluded by .gitignore.
Related MCP server: mcp-file-system-lmstudio
Run the MCP server
Start the MCP server in one terminal and leave it running:
source .venv/bin/activate
.venv/bin/python app.pyThe Streamable HTTP endpoint is now available at:
http://127.0.0.1:8000/mcpKeep that terminal open. Press Ctrl+C when you want to stop the server.
Run the agent notebook
In a second terminal:
cd /Users/somenathmandal/Documents/transfer/grok-playground/MCP
source .venv/bin/activate
jupyter lab mcp_groq_agent.ipynbSelect the .venv Python kernel if Jupyter asks, then run the cells from top to
bottom. The notebook connects directly to http://127.0.0.1:8000/mcp; it never
starts or imports app.py. The Groq API key is loaded from .env and is never
stored in the notebook.
How the pieces connect
Notebook agent
-> Groq OpenAI-compatible API (model reasoning and tool selection)
-> http://127.0.0.1:8000/mcp (tool discovery and execution)
-> app.py MCP toolsThe server must be running before executing the MCP connection cells. If the
notebook reports a connection error, first confirm that the server terminal says
Uvicorn is running on http://127.0.0.1:8000.
The notebook also guards against stale certificate-file environment variables occasionally inherited from notebook launchers. It removes a certificate variable only when the referenced path does not exist; valid custom certificate settings are preserved.
This server cannot be deployed
Maintenance
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