About Jetty
MLC Bakery is a Python service for managing ML datasets and model provenance, aimed at teams tracking entities, activities, and lineage. It supports Croissant metadata validation and dataset search through a search backend.
Key features
- 01Dataset and collection management
- 02Entity tracking
- 03Activity logging
- 04Provenance relationship tracking
- 05Croissant metadata validation
- 06REST API and search
Use cases
- 01Organize and search datasets
- 02Track model-related entities and activities
- 03Validate Croissant metadata
MCP support
The MCP server makes REST calls to the API server, providing MCP clients access to the service.
Configuration
Remote servers need no local install. Pick your client, then copy the config or install in one click.
https://mcp.jetty.io/sseclaude mcp add --transport http mlcbakery https://mcp.jetty.io/sse{"mcpServers":{"mlcbakery":{"type":"http","url":"https://mcp.jetty.io/sse"}}}Open in Cursor{"servers":{"mlcbakery":{"type":"http","url":"https://mcp.jetty.io/sse"}}}Open in VS Code{"mcpServers":{"mlcbakery":{"type":"http","url":"https://mcp.jetty.io/sse"}}}{"mcpServers":{"mlcbakery":{"serverUrl":"https://mcp.jetty.io/sse"}}}Run commands in a terminal; paste JSON into the client’s MCP config and restart. Servers that need sign-in will prompt on first connect.
Tools
Details
From the project READMEMLC Bakery
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 .env -
Start 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 -d -
Run 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 mlcbakery -
Install 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
Comments
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