AI agents are good at reading docs and websites, but giving them direct access to your databases is risky. Pylar solves this by letting you create governed views of your data, publish them as MCP tools to any agent builder, and run evals to track safety and performance.
Define safe, scoped data views on your warehouse. Control exactly what data agents can access with fine-grained permissions.
Learn about data governance →Pylar automatically creates MCP tools from your SQL views, giving agents safe access to structured data without raw database queries.
Learn about MCP integration →Create custom MCP tools on your specific views and publish them to your agent builders with a single link. Share tools across teams and environments.
Learn about custom tools →Monitor agent performance, track query success rates, and measure data usage. Built-in governance and transparency for production AI systems.
Learn about evals →Works with BigQuery, Snowflake, Postgres, Redshift, and more. Model a view once, then publish MCP tools on top—no pipelines, no migration.
SOC 2–ready controls with fine-grained visibility. Limit what agents can query, parameterize inputs, and keep an audit trail for every call.
From a single governed view, generate tools that drop straight into any agent builder. Scale use cases across the org, without ever exposing raw DB access.