loading…
Search for a command to run...
loading…
Ultra-fast to deploy agentic-first MCP-ready semantic layer. Let your data be like water.
Ultra-fast to deploy agentic-first MCP-ready semantic layer. Let your data be like water.
Agent-native analytics. One schema, many surfaces.
Docs · Getting Started · Changelog · Discord · Website
BI tools serve one UI. Bonnard serves everything. Agents over MCP, apps over SDK, dashboards in markdown, internal tools via REST. One set of metric definitions, every consumer gets the same governed answer.
Traditional semantic layers were built for dashboards and retrofitted for AI. Agents get different answers than dashboards, metrics drift across tools, and every new surface means another integration. Bonnard was built agent-native from day one. MCP is a core feature, not a plugin. One CLI, one schema, every consumer gets the same governed answer.
No install required. Run directly with npx:
npx @bonnard/cli init
Or install globally:
npm install -g @bonnard/cli
Then follow the setup flow:
bon init # Scaffold project + agent configs
bon datasource add # Connect your warehouse
bon validate # Check your models locally
bon login # Authenticate
bon deploy -m "initial deploy" # Ship it
Your semantic layer is now live. Agents, dashboards, and the SDK all query the same governed metrics.
No warehouse yet? Start exploring with a full retail demo dataset:
bon datasource add --demo
Requires Node.js 20+.
bon init generates rules and skills for Claude Code, Cursor, and Codex so agents understand your semantic layer from the first prompt.bon mcp, test with bon mcp test.bon dashboard dev, deploy with bon dashboard deploy.bon query and bon schema, or programmatically via the REST API.bon diff), annotations (bon annotate), and full history (bon deployments).bon deploy --ci -m "message" for non-interactive pipelines.Warehouses: Snowflake (including Snowpark), Google BigQuery, Databricks (SQL warehouses and Unity Catalog), PostgreSQL (including Supabase, Neon, and RDS), Amazon Redshift, DuckDB (including MotherDuck)
Data tools: dbt (model and profile import), Dagster, Prefect, Airflow (orchestration), Looker, Cube, Evidence (existing BI layers), SQLMesh, Soda, Great Expectations (data quality)
Bonnard auto-detects your warehouses and data tools. Point it at your project and it discovers schemas, tables, and relationships.
| Command | Description |
|---|---|
bon init |
Scaffold a new project with agent configs |
bon datasource add |
Connect a data source (or --demo for sample data) |
bon validate |
Validate YAML syntax locally |
bon deploy -m "message" |
Deploy to production |
bon pull |
Download deployed models to local project |
bon query |
Run queries (JSON or SQL) |
bon schema |
Explore deployed measures, dimensions, and views |
bon dashboard dev |
Preview a markdown dashboard locally |
bon dashboard deploy |
Deploy a dashboard |
bon mcp |
MCP server setup instructions |
bon keys create |
Create a publishable or secret API key |
bon docs |
Browse documentation from the CLI |
See the full CLI reference for all commands and flags.
| Guide | Description |
|---|---|
| Getting Started | From zero to deployed in minutes |
| CLI Reference | Every command, flag, and option |
| Modeling Guide | Cubes, views, metrics, and dimensions |
| Dashboards | Markdown dashboards with charts, inputs, and theming |
| SDK | TypeScript SDK and React components |
| Querying | JSON and SQL query syntax |
| Changelog | What shipped and when |
Contributions are welcome. If you find a bug or have an idea, open an issue or submit a pull request.
Добавь это в claude_desktop_config.json и перезапусти Claude Desktop.
{
"mcpServers": {
"meal-inc-bonnard-cli": {
"command": "npx",
"args": []
}
}
}