MCP Server
Provides tools to generate OpenAI-backed responses using the Responses API.
Click on "Install 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., "@MCP Serverlist the available records"
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.
MCP Server
This repository contains a minimal Django project that exposes Machine Control Protocol (MCP)-style endpoints for experimenting with OpenAI integrations. The project uses an in-memory store to emulate database records and forwards prompts to the OpenAI Responses API using environment-driven configuration.
Getting started
Install the Python dependencies:
pip install -r requirements.txtConfigure the required environment variables (see below).
Apply migrations and run the test suite to confirm the installation:
python manage.py migrate python manage.py testInstall the frontend dependencies and start the development servers in separate terminals:
# Terminal 1 - Django API python manage.py runserver # Terminal 2 - React development server cd frontend npm install npm run devThe Vite dev server proxies API requests that start with
/mcp/to the Django backend running on port 8000, so no additional CORS configuration is required. Updatefrontend/.env.developmentif your backend runs on a different host or port.Visit
http://127.0.0.1:5173/to use the MCP client interface during development.The MCP endpoint remains available at
http://127.0.0.1:8000/mcp/and accepts JSON-RPC-style POST requests if you want to interact with it directly (for example viacurlor Postman).
Environment variables
The project reads the following environment variables in mcp_server/settings.py:
Variable | Description | Default |
| API key used to authenticate with OpenAI. | Empty string (required for live requests) |
| Base URL for the OpenAI API. Override when routing through compatible gateways. |
|
| Model identifier passed to the Responses API. |
|
At minimum you must provide OPENAI_API_KEY to generate real completions. The tests run without hitting the network by mocking the client.
Fake data model
The interactions app seeds a handful of InteractionRecord entries that mimic database-backed resources. The MCP view exposes operations to list the available records, fetch an individual record, and generate OpenAI-backed responses using either the provided prompt or the record's default prompt.
Frontend configuration
The frontend reads runtime configuration from .env files using the VITE_ prefix. Two presets are provided:
frontend/.env.development– defaults the API base URL to/mcp/and proxies throughhttp://localhost:8000.frontend/.env.production– defaults the API base URL to/mcp/when the React bundle is served by Django.
Override VITE_API_BASE_URL in these files (or create .env.local) to point at a different MCP host.
Building for production
Create an optimised React build and copy it into Django's static directory:
cd frontend
npm run build:djangoThe compiled assets are stored in mcp_server/static/frontend/. Django automatically serves the bundled interface from the root
URL (/) when the build is present. If you prefer to deploy the frontend separately, run npm run build and host the contents of
frontend/dist on your static hosting provider of choice.
Running tests
Execute the Django test runner:
python manage.py testThe unit tests exercise the MCP endpoint and ensure that OpenAI client calls are wired correctly via dependency injection and mocking.
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