django-native-mcp
A small Django-native MCP application and tool registration framework inspired by Celery.
It delegates protocol handling, schemas, validation, serialization, stdio, and Streamable HTTP
to the official mcp Python SDK.
This is not another wrapper around FastMCP, the dependencies are only native Python SDK
Installation
pip install django-native-mcp
Add the application and its configuration:
# settings.py
INSTALLED_APPS = [
# ...
"django_native_mcp",
]
DJANGO_NATIVE_MCP = {
"APP": "config.mcp:app",
}
For a public Streamable HTTP host, configure the SDK host and DNS rebinding allowlists explicitly:
DJANGO_NATIVE_MCP = {
"APP": "config.mcp:app",
"HOST": "0.0.0.0",
"TRANSPORT_SECURITY_SETTINGS": {
"ENABLE_DNS_REBINDING_PROTECTION": True,
"ALLOWED_HOSTS": ["mysite.com"],
"ALLOWED_ORIGINS": ["https://mysite.com"],
},
}
The optional settings preserve the official SDK defaults when omitted.
Streamable HTTP MCP is protected by bearer tokens by default. The package provides an
MCPToken model in the Django admin; its generated 40-character key is sent as
Authorization: Bearer <key>. Tokens support last_used tracking and optional expiration.
Set DJANGO_NATIVE_MCP["DEFAULT_AUTHENTICATION_CLASSES"] to a list of dotted backend paths to
replace the default django_native_mcp.authentication.MCPTokenBackend. Set it to None or []
to make the HTTP MCP endpoint public.
Create the application:
# config/mcp.py
from django_native_mcp import MCP
app = MCP("backend")
app.autodiscover_tools()
Declare tools explicitly in installed Django apps:
# orders/mcp.py
from django_native_mcp import shared_tool
from .models import Order
@shared_tool
async def get_order(order_id: int) -> dict:
"""Get an order."""
order = await Order.objects.aget(pk=order_id)
return {"id": order.pk, "status": order.status}
The registered name is orders.get_order, using the Django application label.
python manage.py mcp_list
python manage.py mcp_inspect orders.get_order
python manage.py mcp_call orders.get_order '{"order_id": 1}'
python manage.py mcp_serve --transport stdio
Tools must use async def. The framework does not add implicit threads or sync_to_async.
Direct application tools
from django_native_mcp import MCP
app = MCP("backend")
@app.tool(name="system.health")
async def health() -> dict:
return {"ok": True}
@app.tool binds immediately to one application. @shared_tool remains application-independent
until autodiscovery binds it.
Streamable HTTP with Django
The official SDK ASGI app can be served alone:
application = app.asgi_app()
Or route /mcp to MCP and everything else to Django:
# config/asgi.py
import os
from django.core.asgi import get_asgi_application
from django_native_mcp.asgi import MCPApplication
os.environ.setdefault("DJANGO_SETTINGS_MODULE", "config.settings")
django_application = get_asgi_application()
# Import the MCP app only after Django's application registry is ready.
from config.mcp import app as mcp_app
application = MCPApplication(
django=django_application,
mcp=mcp_app,
mcp_path="/mcp",
)
The dispatcher forwards ASGI lifespan to the official MCP application, so its transport lifecycle is started and stopped by the outer ASGI server.
Testing
Use the thin wrapper around the official in-process client:
from django_native_mcp.testing import MCPTestClient
async with MCPTestClient(app) as client:
result = await client.call_tool("orders.get_order", {"order_id": 1})
End-to-end example
The example/ directory contains a runnable Django project using the built-in auth
User, a Streamable HTTP MCP endpoint, and a standalone OpenAI Responses API client that discovers
and calls the Django tool through MCP.
Architecture
Django apps / mcp.py
↓
shared_tool → ToolDefinition → ToolRegistry → MCP
↓
official MCPServer
↙ ↘
stdio Streamable HTTP
The registry is process-local and becomes read-only when its official server is created. Each worker builds the same registry from source during startup.
Non-goals
This package is not an MCP protocol implementation, ORM-to-MCP generator, REST/DRF adapter, Celery replacement, background queue, or AI-agent framework. It does not automatically expose Django models and does not infer permissions from them.
License
MIT
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