MLCBakery
FreeNot checkedProvides a bridge between AI assistants and machine learning datasets, enabling search, validation, and retrieval of ML Croissant metadata from the MLCBakery sy
About
Provides a bridge between AI assistants and machine learning datasets, enabling search, validation, and retrieval of ML Croissant metadata from the MLCBakery system without switching contexts.
README
A Python-based service for managing ML model provenance and lineage, built with FastAPI and SQLAlchemy. Support for Croissant metadata validation.
Features
- Dataset management with collection support
- Entity tracking
- Activity logging
- Provenance relationships tracking
- RESTful API endpoints
Running with Docker
Set up Environment Variables: Create a
.envfile in the project root by copying the example:cp env.example .envStart docker containers: The bakery relies on a postgres database and Typesense for search. The MCP server makes REST calls to the API server, which then calls the persistence layer.
docker compose up -dRun Database Migrations: Apply the latest database schema using Alembic.
uv runexecutes commands within the project's managed environment.docker compose exec db psql -U postgres -c "create DATABASE mlcbakery;" docker compose exec api alembic upgrade head
Access the bakery
By default, the API will be available on localhost.
- Swagger UI:
http://bakery.localhost/docs - ReDoc:
http://bakery.localhost/redoc - Streamable MCP HTTP:
http://mcp.localhost/mcp(you may need to add this to your/etc/hostsfor local development)
Running the Server (Locally)
Prerequisites
- Python 3.12+
- uv (Python package manager)
Development steps
Clone the repository:
git clone [email protected]:jettyio/mlcbakery.git cd mlcbakeryInstall Dependencies:
uvusespyproject.tomlto manage dependencies. It will automatically create a virtual environment if one doesn't exist.curl -LsSf https://astral.sh/uv/install.sh | shpip install poetry uvicorn uv run poetry install --no-interaction --no-ansi --no-root --with mcp
Start the FastAPI application using uvicorn:
# Make sure your .env file is present for the DATABASE_URL
uv run uvicorn mlcbakery.main:app --reload --host 0.0.0.0 --port 8000
Authentication
The Bakery is setup to authenticate requests with two methods: JWT Tokens and a "Master Admin Token". Both are configured in the ENV variables (.env file). Both JWT tokens and the Master Admin Token should be provided as "Bearer" Authorization header values.
- ADMIN_AUTH_TOKEN: A fixed value that is the token a user would need to provide to have admin permissions (unrestricted access to all resources).
- JWT_VERIFICATION_STRATEGY: The URL of a trusted JWT token issuer, such as Clerk. We have a development instance of Clerk running that you can use. You can sign up for an account via flows.jetty.io (alpha), or contact [email protected] for access to Jetty's Cloud.
Running Tests
The tests are configured to run against a PostgreSQL database defined by the DATABASE_URL environment variable. You can use the same database as your development environment or configure a separate test database in your .env file if preferred (adjust connection string as needed).
# Ensure DATABASE_URL is set in your environment or .env file
uv run pytest
Installing MLCBakery
This server has no published package — it is built from source. Open the repository and follow its README.
▸ github.com/jettyio/mlcbakeryFAQ
Is MLCBakery MCP free?
Yes, MLCBakery MCP is free — one-click install via Unyly at no cost.
Does MLCBakery need an API key?
No, MLCBakery runs without API keys or environment variables.
Is MLCBakery hosted or self-hosted?
Self-hosted: the server runs locally on your machine via the install command above.
How do I install MLCBakery in Claude Desktop, Claude Code or Cursor?
Open MLCBakery on unyly.org, pick your client tab (Claude Desktop, Claude Code, Cursor) and press Install — the config is generated automatically, no JSON editing.
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