bonnard

Overview

Bonnard provides a self-hosted semantic layer that enables AI agents to query data through the Model Context Protocol. It offers SQL-based metric definitions with caching, access control, and multi-database support. The project includes a Cube semantic layer, pre-aggregation cache, admin UI, deploy API, and health endpoints. It can be deployed as a Docker container and supports various data warehouses including Snowflake, BigQuery, Databricks, PostgreSQL, Redshift, DuckDB, and ClickHouse.

Try asking AI

After installing, here are 3 things you can ask your AI assistant:

• query_data

  Query the semantic layer through the MCP server

  Try:Show me monthly sales for the last yearTry:Get top 10 products by revenueTry:Calculate customer churn rate for Q2
• deploy_models

  Deploy updated semantic models to the server

  Try:Deploy the new customer segment modelTry:Update the sales metrics with latest schemaTry:Apply all pending model changes
• check_health

  Check the health status of the Bonnard server

  Try:Is the server running properly?Try:Check system healthTry:Verify all services are up
• view_schema

  Browse and verify the semantic layer schema

  Try:Show me all available metricsTry:List all cubes and their measuresTry:Verify the customer schema structure

Note: Tool names inferred from documentation of MCP server capabilities, CLI commands, and described functionality. No explicit tool names provided in the README.

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Installation

# 1. Scaffold project
npx @bonnard/cli init --self-hosted

# 2. Configure your data source
#    Edit .env with your database credentials

# 3. Start the server
docker compose up -d

# 4. Define your semantic layer
#    Add cube/view YAML files to bonnard/cubes/ and bonnard/views/

# 5. Deploy models to the server
bon deploy

# 6. Connect AI agents
bon mcp

{
  "mcpServers": {
    "bonnard": {
      "url": "https://bonnard.example.com/mcp",
      "headers": {
        "Authorization": "Bearer your-secret-token-here"
      }
    }
  }
}

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