# Django MCP Server
[](https://pypi.org/project/django-mcp-server/)
[](https://djangopackages.org/packages/p/django-mcp-server/)
[](https://pypi.org/project/django-mcp-server/)
**Django MCP Server** is an implementation of the **Model Context Protocol (MCP)** extension for Django. This module allows **MCP Clients** and **AI agents** to interact with **any Django application** seamlessly.
π Django-Style declarative style tools to allow AI Agents and MCP clients tool to interact with Django.
π Expose Django models for AI Agents and MCP Tools to query in 2 lines of code in a safe way.
π Convert Django Rest Framework APIs to MCP tools with one annotation.
β
Working on both WSGI and ASGI without infrastructure change.
β
Validated as a Remote Integration with Claude AI.
π€ Any MCP Client or AI Agent supporting MCP , (Google Agent Developement Kit, Claude AI, Claude Desktop ...) can interact with your application.
Many thanks π to [all the contributor community](https://github.com/omarbenhamid/django-mcp-server/graphs/contributors)
Maintained β¨ with care by [Smart GTS software engineering](https://www.smart-gts.com/#contact).
Licensed under the **MIT License**.
---
## Features
- Expose Django models and logic as **MCP tools**.
- Serve an MCP endpoint inside your Django app.
- Easily integrate with AI agents, MCP Clients, or tools like Google ADK.
---
## Quick Start
### 1οΈβ£ Install
```bash
pip install django-mcp-server
```
Or directly from GitHub:
```bash
pip install git+https://github.com/omarbenhamid/django-mcp-server.git
```
---
### 2οΈβ£ Configure Django
β
Add `mcp_server` to your `INSTALLED_APPS`:
```python
INSTALLED_APPS = [
# your apps...
'mcp_server',
]
```
β
Add the **MCP endpoint** to your `urls.py`:
```python
from django.urls import path, include
urlpatterns = [
# your urls...
path("", include('mcp_server.urls')),
]
```
By default, the MCP endpoint will be available at `/mcp`.
---
### 3οΈβ£ Define MCP Tools
In mcp.py create a subclass of `ModelQueryToolset` to give access to a model :
```python
from mcp_server import ModelQueryToolset
from .models import *
class BirdQueryTool(ModelQueryToolset):
model = Bird
def get_queryset(self):
"""self.request can be used to filter the queryset"""
return super().get_queryset().filter(location__isnull=False)
class LocationTool(ModelQueryToolset):
model = Location
class CityTool(ModelQueryToolset):
model = City
```
Or create a sub class of `MCPToolset` to publish generic methods (private _ methods are not published)
Example:
```python
from mcp_server import MCPToolset
from django.core.mail import send_mail
class MyAITools(MCPToolset):
def add(self, a: int, b: int) -> list[dict]:
"""A service to add two numbers together"""
return a+b
def send_email(self, to_email: str, subject: str, body: str):
""" A tool to send emails"""
send_mail(
subject=subject,
message=body,
from_email='your_email@example.com',
recipient_list=[to_email],
fail_silently=False,
)
```
---
### Verify with MCP Inspect
Use the management commande mcp_inspect to ensure your tools are correctly declared :
```bash
python manage.py mcp_inspect
```
### Use the MCP with any MCP Client
The mcp tool is now published on your Django App at `/mcp` endpoint.
**IMPORTANT** For production setup, on non-public data, consider enabling
authorization through : DJANGO_MCP_AUTHENTICATION_CLASSES
### Test with MCP Python SDK
You can test it with the python mcp SDK :
```python
from mcp.client.streamable_http import streamablehttp_client
from mcp import ClientSession
async def main():
# Connect to a streamable HTTP server
async with streamablehttp_client("http://localhost:8000/mcp") as (
read_stream,
write_stream,
_,
):
# Create a session using the client streams
async with ClientSession(read_stream, write_stream) as session:
# Initialize the connection
await session.initialize()
# Call a tool
tool_result = await session.call_tool("get_alerts", {"state": "NY"})
print(tool_result)
if __name__ == "__main__":
import asyncio
asyncio.run(main())
```
Replace `http://localhost:8000/mcp` by the acutal Django host and run this cript.
### Use from Claude AI
As of **June 2025** Claude AI support now MCPs through streamable HTTP protocol with preè-requisites :
*
* Setup OAuth2, for example :
* Install [Django Oauth Toolkit](https://django-oauth-toolkit.readthedocs.io/en/latest/))
* Include `'oauth2_provider.contrib.rest_framework.OAuth2Authentication'` in `DJANGO_MCP_AUTHENTICATION_CLASSES` in `settings.py`
* Claude AI requires Dynamic Client Registration. as of today [it is not supported by django oauth toolkit](github.com/jazzband/django-oauth-toolkit/issues/670) but you can use [This Django Oauth Toolkit DCR Add-On](https://github.com/omarbenhamid/django-oauth-toolkit-dcr)
* Unless you implement OAuth server Metadata RFC correctly, you need to keep OAuth2 URLS (`/register`, `/token` and `/authorize` at their default location).
### Test in Claude Desktop
You can [test MCP servers in Claude Desktop](https://modelcontextprotocol.io/quickstart/server). As for now
claude desktop only supports local MCP Servers. So you need to have your app installed on the same machine, in a
dev setting probably.
For this you need :
1. To install Claude Desktop from [claude.ai](https://claude.ai)
2. Open File > Settings > Developer and click **Edit Config**
3. Open `claude_desktop_config.json` and setup your MCP server :
```json
{
"mcpServers": {
"test_django_mcp": {
"command": "/path/to/interpreter/python",
"args": [
"/path/to/your/project/manage.py",
"stdio_server"
]
}
}
```
**NOTE** `/path/to/interpreter/` should point to a python interpreter you use (can be in your venv for example)
and `/path/to/your/project/` is the path to your django project.
## Advanced topics
### Publish Django Rest Framework APIs as MCP Tools
You can use `drf_publish_create_mcp_tool` / `drf_publish_update_mcp_tool` / `drf_publish_delete_mcp_tool` /
`drf_publish_list_mcp_tool` as annotations or method calls to register DRF CreateModelMixin / UpdateModelMixin
/ DestroyModelMixin / ListModelMixin based views to MCP tools seamlessly. Django MCP Server will generate the schemas
to allow MCP Clients to use them.
**NOTE** in some *older DRF versions* schema generation is not supported out of the box, you should then provide to the registration
annotation the
```python
from mcp_server import drf_publish_create_mcp_tool
@drf_publish_create_mcp_tool
class MyModelView(CreateAPIView):
"""
A view to create MyModel instances
"""
serializer_class=MySerializer
```
notice that the docstring of the view is used as instructions for the model.
You can better tune this like :
```python
@drf_publish_create_mcp_tool(instructions="Use this view to create instances of MyModel")
class MyModelView(CreateAPIView):
"""
A view to create MyModel instances
"""
serializer_class=MySerializer
```
Finally, you can register after hand in mcp.py for example with:
```python
drf_publish_update_mcp_tool(MyDRFAPIView, instructions="Use this tool to update my model, but use it with care")
```
**IMPORTANT**
Notice that **builti-in authentication classes are disabled** by default along with filter_backends, permission_classes and pagination_class, that's because
the MCP authentication is used.
Since the pagination_class is also disabled, you will need to account for that if you're using an existing paginated DRF view (`self.paginator` will be `None`).
### Django Rest Framework Serializer integration
You can annotate a tool with `drf_serialize_output(...)` to serialize its output using
django rest framework, like :
```python
from mcp_server import drf_serialize_output
from .serializers import FooBarSerializer
from .models import FooBar
class MyTools(MCPToolset):
@drf_serialize_output(FooBarSerializer)
def get_foo_bar():
return FooBar.objects.first()
```
### Use low level mcp server annotation
You can import the DjangoMCP server instance and use FastMCP annotations to declare
mcp tools and resources :
```python
from mcp_server import mcp_server as mcp
from .models import Bird
@mcp.tool()
async def get_species_count(name: str) -> int:
'''Find the ID of a bird species by name (partial match). Returns the count.'''
ret = await Bird.objects.filter(species__icontains=name).afirst()
if ret is None:
ret = await Bird.objects.acreate(species=name)
return ret.count
@mcp.tool()
async def increment_species(name: str, amount: int = 1) -> int:
'''
Increment the count of a bird species by a specified amount.
Returns the new count.
'''
ret = await Bird.objects.filter(species__icontains=name).afirst()
if ret is None:
ret = await Bird.objects.acreate(species=name)
ret.count += amount
await ret.asave()
return ret.count
```
β οΈ **Important**:
1. Always use **Django's async ORM API** when you define async tools.
2. Be careful not to return a QuerySet as it will be evaluated asynchroniously which would create errors.
### Customize the default MCP server settings
In `settings.py` you can initialize the `DJANGO_MCP_GLOBAL_SERVER_CONFIG` parameter. These will be
passed to the `MCPServer` server during initialization
```python
DJANGO_MCP_GLOBAL_SERVER_CONFIG = {
"name":"mymcp",
"instructions": "Some instructions to use this server",
"stateless": False
}
```
### Session management
By default the server is statefull, and state is managed as [Django session](https://docs.djangoproject.com/en/5.2/topics/http/sessions/)
`request.session` object, so the session backend must thus be set up correctly. The
request object is available in `self.request` for class based toolsets.
**NOTE** The session middleware is not required to be set up as MCP sessions are managed
independently and without cookies.
.
You can make the server stateless by defining : `DJANGO_MCP_GLOBAL_SERVER_CONFIG`
**IMPORTANT** state is managed by django sessions, if you use low level `@mcp_server.tool()` annotation for example
the behaviour of preserving the server instance accross calls of the base python API is not preserved due to architecture
of django in WSGI deployments where requests can be served by different threads !
### Authorization
The MCP endpoint supports [Django Rest Framework authorization classes](https://www.django-rest-framework.org/api-guide/authentication/)
You can set them using `DJANGO_MCP_AUTHENTICATION_CLASSES` in `settings.py` ex. :
```python
DJANGO_MCP_AUTHENTICATION_CLASSES=["rest_framework.authentication.TokenAuthentication"]
```
**IMPORTANT** Now the [MCP Specification version 2025-03-26](https://modelcontextprotocol.io/specification/2025-03-26/basic/authorization)
advices to use an OAuth2 workflow, so you should integrate
[django-oauth-toolkit with djangorestframework integration](https://django-oauth-toolkit.readthedocs.io/en/latest/rest-framework/getting_started.html)
setup, and use `'oauth2_provider.contrib.rest_framework.OAuth2Authentication'` in
`DJANGO_MCP_AUTHENTICATION_CLASSES`. Refer to [the official documentation of django-oauth-toolkit](https://django-oauth-toolkit.readthedocs.io/en/latest/rest-framework/getting_started.html)
### Advanced / customized setup of the view
You can in your urls.py mount the MCPServerStreamableHttpView.as_view() view and customize it with any extra parameters.
### Custom output format (renderers) for ModelQueryToolset
You can define any [DRF rendrer](https://www.django-rest-framework.org/api-guide/renderers/) to produce output, for this
it must be declared in your settings:
```python
DJANGO_MCP_OUTPUT_RENDERER_CLASSES = [
"rest_framework.renderers.JSONRenderer",
"rest_framework_csv.renderers.CSVRenderer"
]
```
Then in your `ModelQueryToolset` declaration you can add
```python
...
output_format="csv"
```
further you can instruct the tool to attach the result as an [MCP Embedded Resource] rather than direct return with
```python
...
output_as_resource=True
```
*NOTE* some renderers like `drf-excel` are designed in a way that does not allow using them outside of DRF View, they will not
work here..
### Secondary MCP endpoint
in `mcp.py`
```python
from mcp_server.djangomcp import DjangoMCP
second_mcp = DjangoMCP(name="altserver")
@second_mcp.tool()
async def my_tool():
...
```
in urls.py
```python
...
from yourapp.mcp import second_mcp
...
path("altmcp", MCPServerStreamableHttpView.as_view(mcp_server=second_mcp))
...
```
**IMPORTANT** When you do this the DJANGO_MCP_AUTHENTICATION_CLASSES settings is **ignored** and
your view is unsecure. You **SHOULD** [Setup DRF Authentication](https://www.django-rest-framework.org/api-guide/authentication/)
for your view, for exemple :
```python
...
MCPServerStreamableHttpView.as_view(permission_classes=[IsAuthenticated], authentication_classes=[TokenAuthentication])
...
```
## Testing
### The server
You can setup you own app or use the [mcpexample django app](examples/mcpexample) app.
### The client
By default, your MCP Server will be available as a
[stateless streamable http transport](https://modelcontextprotocol.io/specification/2025-03-26/basic/transports#streamable-http)
endpoint at /mcp (ex. http://localhost:8000/mcp) (*without / at the end !).
There are many ways to test :
1. Using the test [MCP Client script : test/test_mcp_client.py](test/test_mcp_client.py)
2. You can test using [MCP Inspector tool](https://github.com/modelcontextprotocol/inspector)
3. or any compatible MCP Client like google agent developement kit.
---
## Integration with Agentic Frameworks and MCP Clients
### Google Agent Developement Kit Example
**NOTE** as of today the [official google adk does not support StreamableHTTP Transport](https://github.com/google/adk-python/issues/479)
but you could use [this fork](https://github.com/omarbenhamid/google-adk-python)
Then you can use the [test agent in test/test_agent](test/test_agent/agent.py) with by
starting `adk web` in the `test` folder. Make sure first :
1. Install adk with streamablehttp support : `pip install git+https://github.com/omarbenhamid/google-adk-python.git`
2. Start a django app with an MCP endpoint : `python manage.py runserver` in the `examples/mcpexample` folder.
2. If you use TokenAuthorization create an access token, for example in Django Admin of your app.
3. Setup in `test/test_agent/agent.py` the right endpoint location and authentication header
4. Enter the `test` folder.
5. Run `adk web`
6. In the shell you can for example use this prompt : "I saw woody woodpecker, add it to my inventory"
### Other clients
You can easily plug your MCP server endpoint into any agentic framework supporting MCP streamable http servers.
Refer to this [list of clients](https://modelcontextprotocol.io/clients)
---
## Settings
- **DJANGO_MCP_GLOBAL_SERVER_CONFIG** a configuration dictionnary for the global MCP server default to empty. It can include the following parmaters
- name: a name for the server
- instructions: global instructions
- stateless : when set to 'True' the server will not manage sessions
- **DJANGO_MCP_AUTHENTICATION_CLASSES** (default to no authentication) a list of reference to Django Rest Framework authentication classes to enfors in the main MCP view.
- **DJANGO_MCP_GET_SERVER_INSTRUCTIONS_TOOL** (default=True) if true a tool will be offered to obtain global instruction and tools will instruct the agent to use it, as agents do not always have the MCP server global instructions included in their system prompt.
- **DJANGO_MCP_ENDPOINT** (default="mcp") a string indicating the url endpoint used by the server. If you want it to have a trailing slash, for example, set it to "mcp/"
## Roadmap
- β
**Stateless streamable HTTP transport** (implemented)
- π **STDIO transport integration for dev configuration (ex. Claude Desktop)**
- π ****
- π **Stateful streamable HTTP transport using Django sessions**
- π **SSE endpoint integration (requires ASGI)**
- π **Improved error management and logging**
---
## Issues
If you encounter bugs or have feature requests, please open an issue on [GitHub Issues](https://github.com/omarbenhamid/django-mcp-server/issues).
---
## License
MIT License.