What teams use dvt for
Every way teams put dvt to work, in their own terms: AI-native BI, dashboards over MCP, dashboards as code, and AI dashboards for Snowflake.
AI-native BI tool for agentic dashboards
dvt is an AI-native BI tool: dashboards are versioned JSON specs that humans and agents author the same way, the object model agentic BI needs.
Build dashboards with Claude Code
Install the dvt Claude Code plugin and build dashboards with Claude Code over MCP: the agent validates, previews, and applies a live JSON spec.
MCP dashboard tool for AI agents
dvt is an MCP dashboard tool: agents validate, apply with preview, render, diff, comment, and export dashboards through one MCP server, no GUI required.
AI dashboards for Snowflake
dvt builds AI dashboards for Snowflake: agents query your tables over MCP, author a versioned spec, and can run entirely inside your account as a Native App.
Dashboards as code: the dvt implementation
Dashboards as code means a dashboard is a versioned, schema-validated JSON document you can diff and roll back, not GUI state. dvt implements it natively.
AI dashboards for Postgres
dvt builds AI dashboards for Postgres: an agent writes SQL against your database, authors a versioned JSON spec, and dvt runs every query on your server.
AI dashboards for BigQuery
dvt builds AI dashboards for BigQuery: an agent writes SQL against your datasets, authors a versioned JSON spec, and every query runs in your own project.
Dashboards from Google Sheets
dvt builds live dashboards from Google Sheets: an agent authors a versioned JSON spec over your spreadsheet, and dvt reads the sheet itself, never a copy.