django-ai-boost

Django AI Boost is an MCP server that exposes Django project information to AI assistants using the MCP tools. It provides read-only endpoints for discovering models, URLs, management commands, database schema, migrations, and Django settings, making it easier for agents to understand and interact with a Django codebase. The server is designed to work with a Django project and can be run locally with the appropriate Django settings module. Once running, you can access the MCP tools to browse project structure, inspect models and URLs, read recent logs, and query configuration values, all without modifying your codebase.

To use it, install the server via uv (Python/uv) or run it directly if installed as a console script. Typical workflows involve configuring environment variables such as the Django settings module and Python path to point at your Django project, then invoking the django-ai-boost command. The tools are read-only and safe for integration with AI assistants, providing an efficient way to provide context about your Django application to code-generating or planning agents.

Prerequisites:

• Python 3.8+ and a compatible Django project
• uv (Python package manager) or a Python environment able to install the package

Installation steps (recommended):

1. Install uv if you haven't already (Unix-like systems):

curl -LsSf https://astral.sh/uv/install.sh | sh

2. Install the package (development or production):

uv install django-ai-boost

• Alternatively, install via pip in a virtual environment:

python -m venv .venv
source .venv/bin/activate  # on Windows: .venv\Scripts\activate
pip install django-ai-boost

3. Verify installation:

uv run django-ai-boost --help

4. Run the server (example):

export DJANGO_SETTINGS_MODULE=myproject.settings
# or pass inline settings if your tool supports it
django-ai-boost --settings myproject.settings

Notes:

• The Django project must be accessible to the server (settings module and Python path may need to be adjusted).
• If you prefer not to use uv, you can install the package in a standard Python environment and run the provided console script (e.g., django-ai-boost) directly.

Tips and common issues:

• Ensure DJANGO_SETTINGS_MODULE points to a valid Django settings module for your project.
• If running behind a network proxy, configure environment variables accordingly for outbound requests.
• The MCP tools expose read-only information; for production deployments with authentication, enable bearer token support over the SSE transport.
• If you use uv, you can specify PYTHONPATH to point to your Django project root so imports resolve correctly.
• When running in production with SSE transport, consider enabling TLS/HTTPS and restricting access via firewall or reverse proxy.
• If you encounter module import errors, verify that the virtual environment or Python path contains your Django project and its dependencies.