Config is the same across clients — only the file and path differ.
{
"mcpServers": {
"bonnard-cli": {
"args": [
"-y",
"@bonnard/cli"
],
"command": "npx"
}
}
}Are you the author?
Add this badge to your README to show your security score and help users find safe servers.
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.
This server supports HTTP transport. Be the first to test it — help the community know if it works.
Five weighted categories — click any category to see the underlying evidence.
No known CVEs.
Checked @bonnard/cli against OSV.dev.
Be the first to review
Have you used this server?
Share your experience — it helps other developers decide.
Sign in to write a review.
Others in analytics / ai-ml
Persistent memory using a knowledge graph
MCP Server for GCP environment for interacting with various Observability APIs.
Privacy-first. MCP is the protocol for tool access. We're the virtualization layer for context.
An open-source AI agent that brings the power of Gemini directly into your terminal.
MCP Security Weekly
Get CVE alerts and security updates for Bonnard Cli and similar servers.
Start a conversation
Ask a question, share a tip, or report an issue.
Sign in to join the discussion.
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),