Quickstart
sqldash keeps your dashboards, metrics, and data agents as YAML files in the repos that own them. These steps take you from nothing to a running dashboard, then to your own data and your own agent, in a few minutes.
Run the demo
uvx sqldash init --demo
uvx sqldash serve
init --demo creates a .sqldash/ folder with a sample dashboard, a metrics.yaml, an
agents.yaml, and a generated orders CSV. serve opens the dashboard in your browser.
The demo runs on DuckDB over the local CSV, so there is no warehouse to connect, no credentials, and nothing to set up.
Change something
Open .sqldash/demo.yaml in your editor, change a tile’s chart: bar to chart: pie,
and save. The dashboard updates in place.
You can make the same kind of change from the browser. Click Edit to drag, resize, or delete tiles, or + Explore to write a query, chart it, and add it as a new tile, and sqldash writes each change back to the file. For changes you would rather describe than click, open AI Studio and ask your coding agent.
Connect your warehouse
Install sqldash with the driver for your warehouse, then run setup inside the project.
uv tool install 'sqldash[snowflake]'
sqldash setup
setup asks for the connection details, keeps your credentials out of the repo, and
tests the connection. Setup covers every flag, and
Sources covers every warehouse.
Give your agent the metrics
Add sqldash as an MCP server in your coding agent, from inside the project.
codex mcp add sqldash -- sqldash mcp "$PWD"
claude mcp add sqldash -- sqldash mcp "$PWD"
Now your agent can answer questions from the same governed metrics your dashboards use.
These commands use the sqldash you installed with your warehouse driver, so the MCP
server has the driver too. "$PWD" saves the project’s absolute path, which matters
because Codex keeps the setting for every project. If you are still on the demo and
never installed sqldash, use uvx sqldash mcp "$PWD" instead.