MCP Hub
Back to servers

bonnard

Open-source agentic schema for reliable data outputs. Query data through MCP and via our SDK. Create apps, embed data or just simply explore through your preferred agent.

GitHub
Stars
12
Forks
1
Updated
Mar 3, 2026
Validated
Mar 6, 2026

Bonnard: agent-native analytics. One schema, many surfaces.

Self-hosted semantic layer for AI agents.

Apache 2.0 License Docker Discord

Docs · CLI · Discord · Website


Bonnard is an agent-native semantic layer — one set of metric definitions, every consumer (AI agents, apps, dashboards) gets the same governed answer. This repo is the self-hosted Docker deployment: run Bonnard on your own infrastructure with no cloud account needed.

Quick Start

# 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. Verify your semantic layer
bon schema

# 7. Connect AI agents
bon mcp

Requires Node.js 20+ and Docker.

What's Included

  • MCP server — AI agents query your semantic layer over the Model Context Protocol
  • Cube semantic layer — SQL-based metric definitions with caching, access control, and multi-database support
  • Cube Store — pre-aggregation cache for fast analytical queries
  • Admin UI — browse deployed models, views, and measures at http://localhost:3000
  • Deploy API — push model updates via bon deploy without restarting containers
  • Health endpointGET /health for uptime monitoring

Connecting AI Agents

Run bon mcp to see connection config for your setup. Examples below.

Claude Desktop / Cursor

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

Claude Code

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

CrewAI (Python)

from crewai import MCPServerAdapter

mcp = MCPServerAdapter(
    url="https://bonnard.example.com/mcp",
    transport="streamable-http",
    headers={"Authorization": "Bearer your-secret-token-here"}
)

Production Deployment

Authentication

Protect your endpoints by setting ADMIN_TOKEN in .env:

ADMIN_TOKEN=your-secret-token-here

All API and MCP endpoints will require Authorization: Bearer <token>. The /health endpoint remains open for monitoring.

Restart after changing .env:

docker compose up -d

TLS with Caddy

Caddy provides automatic HTTPS via Let's Encrypt.

Create a Caddyfile next to your docker-compose.yml:

bonnard.example.com {
    reverse_proxy localhost:3000
}

Add Caddy to your docker-compose.yml:

  caddy:
    image: caddy:2
    ports:
      - "80:80"
      - "443:443"
    volumes:
      - ./Caddyfile:/etc/caddy/Caddyfile:ro
      - caddy_data:/data
    restart: unless-stopped

Add the volume at the top level:

volumes:
  models: {}
  caddy_data: {}

Then remove the Bonnard port mapping (ports: - "3000:3000") since Caddy handles external traffic.

Deploy to a VM

# Copy project files to your server
scp -r . user@your-server:~/bonnard/

# SSH in and start
ssh user@your-server
cd ~/bonnard
docker compose up -d

Configuration

VariableDescriptionDefault
CUBEJS_DB_TYPEDatabase driver (postgres, duckdb, snowflake, bigquery, databricks, redshift, clickhouse)duckdb
CUBEJS_DB_*Database connection settings (host, port, name, user, pass)
CUBEJS_DATASOURCESComma-separated list for multi-datasource setupsdefault
CUBEJS_API_SECRETHS256 secret for Cube JWT auth (auto-generated by bon init)
ADMIN_TOKENBearer token for API/MCP authentication— (open)
CUBE_PORTCube API port4000
BONNARD_PORTBonnard server port3000
CORS_ORIGINAllowed CORS origins*
CUBE_VERSIONCube Docker image tagv1.6
BONNARD_VERSIONBonnard Docker image taglatest

See .env.example for a full annotated configuration file.

Architecture

ServiceImageRole
cubecubejs/cubeSemantic layer engine — executes queries against your warehouse
cubestorecubejs/cubestorePre-aggregation cache — stores materialized results for fast reads
bonnardghcr.io/bonnard-data/bonnardMCP server, admin UI, deploy API — the interface layer for agents and tools

All three services communicate over an internal Docker network. Only bonnard (port 3000) and optionally cube (port 4000) are exposed externally.

Monitoring

# Health check
curl http://localhost:3000/health

# View logs
docker compose logs -f

# View active MCP sessions
curl -H "Authorization: Bearer <token>" http://localhost:3000/api/mcp/sessions

Deploying Schema Updates

From your development machine:

bon deploy

This pushes your cube/view YAML files to the running server. No restart needed — Cube picks up changes automatically.

Pinning Versions

Control image versions via .env:

CUBE_VERSION=v1.6
BONNARD_VERSION=latest

Supported Data Sources

Warehouses: Snowflake, Google BigQuery, Databricks, PostgreSQL (including Supabase, Neon, RDS), Amazon Redshift, DuckDB (including MotherDuck), ClickHouse

See the full documentation for connection guides.

Ecosystem

  • @bonnard/cli — scaffold projects, deploy models, connect agents
  • @bonnard/sdk — query the semantic layer from JavaScript/TypeScript
  • @bonnard/react — React chart components and dashboard viewer

Community

License

Apache 2.0

Reviews

No reviews yet

Sign in to write a review