Django MCP Streamable Http Server View

Django MCP Streamable Http Server View

Example of an MCP protocol streaming HTTP server view implemented based on the Django framework

Developer tools Artificial intelligence chatbots #Django #MCP protocol #HTTP service #View example .Python

rating : 2 points

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update time : 2025-04-28

What is the Django MCP Streamable HTTP Server?

This is a Django server that implements the Model Context Protocol (MCP) specification, allowing clients to interact with AI models via the HTTP protocol. The server supports streaming responses, making it suitable for handling long-running model inference tasks.

How to use the Django MCP Streamable HTTP Server?

Simply copy the provided views.py file into your Django application and configure the corresponding URL routes to quickly set up a server endpoint that complies with the MCP specification.

Use cases

Suitable for scenarios where AI models need to be served via the standard HTTP protocol, especially AI applications that require support for long-running tasks and streaming responses.

Main features

MCP specification compliance

Fully implemented in accordance with the Model Context Protocol (MCP) 2025 - 03 - 26 specification

Streaming HTTP response

Supports chunked transfer encoding for real - time streaming responses

Seamless Django integration

Implemented as a standard Django view, easy to integrate with existing Django projects

Advantages

Rapid deployment: Copy a single file to get full MCP server functionality

Standardization: Follows the MCP specification to ensure compatibility with other MCP clients

Flexibility: Based on the Django framework, easy to extend and customize

Limitations

Only supports the HTTP protocol and does not support other transport protocols in the MCP specification

Requires a Django environment and is not suitable for lightweight deployment scenarios

The implementation of streaming responses depends on Django's StreamingHttpResponse

How to use

Install dependencies

Ensure that the Django framework and necessary Python dependencies are installed

Add view file

Copy the provided views.py file into your Django application

Configure URL routes

Add route configuration for the MCP endpoint in urls.py

Start the server

Run the Django development server or deploy it to a production environment

Usage examples

Text generation task

Use the MCP server to handle long text generation tasks and obtain the generated results in real - time

Image processing task

Process image recognition tasks through the MCP server and return the recognition results in stages

Frequently Asked Questions

Which models does the MCP server support?

How to handle large file uploads?

How to extend the server functionality?

Related resources

MCP specification document

Official specification document of the Model Context Protocol

GitHub repository

Project source code and examples

Django documentation

Official documentation of the Django framework

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