What is Pylar?

Pylar is a security-first platform that sits between your AI agents and your databases. It lets you define exactly what data agents can access by creating SQL views, then automatically turns those views into custom MCP tools. Once published, those tools connect to any agent builder—Cursor, n8n, Langchain, or others—with a single secure link. Every query is logged, giving you full observability across every AI deployment.

Who it's for

• Data engineers who need to expose production data to agents without writing custom API endpoints or managing authentication.
• Security teams that want to enforce row-level security and column-level filtering so agents never touch raw tables.
• AI platform leads who need to ship agent integrations fast—from zero to production in under 48 hours, according to one user.

Key features

SQL views as the only access layer

Agents never query raw tables. You write SQL views that filter sensitive columns, implement row-level security, and even join across different databases like Postgres and Snowflake. The view is the only surface area agents can see.

One-click MCP tool generation

From any view, you can create multiple MCP tools using natural language or manual configuration. Pylar's AI assistant can generate tools like fetch_error_codes or list_user_activities in seconds. No API code to write.

Publish once, connect anywhere

Tools are published to a single secure link. That link works with any agent builder—Cursor, n8n, Langchain, or custom frameworks. Update a view or tool definition once, and every connected agent picks up the change immediately. No redeploying.

Full observability across deployments

Every query an agent runs is logged. You can see which tools were called, what data was returned, and how compute costs are trending. This gives you a single pane of glass for all AI data access.

What stands out

"Write one SQL view, Pylar spits out the tools, hook it into Cursor. Takes maybe 10 minutes."

That speed is the core differentiator. Traditional approaches require building REST endpoints, managing authentication tokens, and redeploying every time data definitions change. Pylar collapses that workflow into a single view definition. Teams that used to spend weeks on API scaffolding now ship agent integrations in hours. The security model—views as the only access layer—also eliminates the need for per-tool permissioning, because the database already enforces what the view allows.

Worth checking out if…

You're shipping AI agents that need live access to production databases, and your security team won't let agents connect directly to Snowflake or Postgres. Pylar is also a strong fit if you're tired of writing and maintaining custom API endpoints for every new agent capability, or if you need to give multiple agent builders (Cursor, n8n, Langchain) access to the same curated data without duplicating work.