The simplest, safest way to connect agents to your data stack

Pylar sits between your agents and your databases. You define what data they can access, build custom tools on top of it, and get full observability across all your AI deployments.

Data sources

Agents

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Trusted by teams shipping production AI

Security wouldn't let us hook agents straight into Snowflake. Can't say I blame them honestly. Pylar fixed it though. We just tell it what's safe to share, agents get what they need, and our compute costs stay predictable.

Sarah Li

Head of Engineering

We've got Postgres and Snowflake connected, merged our customer info from both, and suddenly our n8n and Langchain agents are doing real work. Knocked out 5 tools in one afternoon. Zero API code.

Michael Chen

Head of Data

Used to be weeks of work. APIs, endpoints, all that auth stuff. Now? Write one SQL view, Pylar spits out the tools, hook it into Cursor. Takes maybe 10 minutes.

Elena Marquez

Head of AI Platform

Pylar's basically our control center now. Tweak a view? Agents pick it up right away. Messed up a column? Fix it once, everyone sees the update. No more redeploying anything.

Josh L

Head of RevOps

We wanted to put an AI agent on top of our SaaS platform for customers. Security was the big worry. Pylar lets us sandbox everything and set exactly how the AI can touch our data. Went live pretty fast.

David Kim

CTO

Forty eight hours from zero to production. That's it. Our agents are answering customer questions using real info. Pylar did the heavy lifting on views, tools, everything. Pretty wild.

Priya Patel

VP of Product

How Pylar Works

Connect to your sources, sandbox the data you want exposed, compile it into agent-ready tools, and publish to any agent builder with one secure link.

Views are the only access level. Agents query through your SQL views, never raw tables. Filter sensitive data, implement row-level security, join across databases.

Learn about views →

SQL Editor

1CREATE VIEW customer_analytics AS

2SELECT

3 c.customer_id,

4 c.email,

5 c.company_name,

6 t.ticket_count,

7 t.avg_resolution_time,

8 u.monthly_usage,

9 u.feature_adoption

10FROM customers c

11LEFT JOIN tickets t

12 ON c.customer_id = t.customer_id

13LEFT JOIN usage_metrics u

14 ON c.customer_id = u.customer_id

15WHERE c.status = 'active'

16 AND u.last_activity > '2024-01-01';

Results

customer_id

company_name

ticket_count

avg_resolution

monthly_usage

MCP TOOLS

◆fetch_customerEdit

◆list_contactsEdit

◆fetch_user_analyticsEdit

◆list_revenue_forecastEdit

◆fetch_error_trendsEdit

◆list_churn_analysisEdit

◆fetch_conversion_funnelEdit

◆list_product_usageEdit

AGENT VIEWS

◆support_errorsPublished

◆user_analyticsDraft

◆error_trendsPublished

◆revenue_forecastPublished

Views are the only access level. Agents query through your SQL views, never raw tables. Filter sensitive data, implement row-level security, join across databases.

Create MCP tools from views using natural language or manual config. Build multiple tools per view. Publish once, connect to any agent builder.

Explore MCP tools →

IN PROGRESS 3

fetch_error_codes

Generating

fetch_product_events

Generating

fetch_support_tickets

Generating

READY FOR REVIEW 3

✓

fetch_customer_data

24m

Ready for agent integration...

✓

get_analytics_summary

59m

+37 -0 Analytics tool ready...

✓

list_user_activities

+12 -3 User activity tool...

Create MCP Tools for Support Data

Generate MCP tools to help agents access support ticket data safely.

You

Create MCP tools for our support data. I need tools to fetch error codes, product events, and support tickets.

Pylar AI

I'll create three MCP tools from your support views:

📄

fetch_error_codes+15-0

Error codes tool with error_type parameter

📄

fetch_product_events+12-0

Product events tool with date_range parameter

📄

fetch_support_tickets+18-0

Support tickets tool with status parameter

All set! Three MCP tools created and ready for agent integration.

Create MCP tools from views using natural language or manual config. Build multiple tools per view. Publish once, connect to any agent builder.

Publish once, connect everywhere. Get one MCP server URL and token—use it to connect all your tools to any agent builder. Update tools in Pylar, changes reflect automatically everywhere.

Learn about publishing →

Publish Tools

3 tools ready to publish

✓

get_customer_by_id

customer_360

✓

list_high_value_customers

customer_360

✓

get_customer_engagement

customer_360

Connection Credentials

MCP HTTP Stream URL

https://mcp.publish.pylar.ai/mcp

Authorization Bearer Token

Bearer eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9...

Connected Builders

Claude Desktopconnected

Cursorconnected

Windsurfconnected

VS Codeconnected

LangGraphconnected

OpenAI Platformconnected

Zapierconnected

Makeconnected

n8nconnected

Changes to tools reflect automatically in all connected builders

Publish once, connect everywhere. Get one MCP server URL and token—use it to connect all your tools to any agent builder. Update tools in Pylar, changes reflect automatically everywhere.

Track success rates, analyze errors, understand query patterns. Use Evals to refine views and tools without redeploying agents.

See how it works →

Agent View Evals

support.support_customer_context

93.1%

Success Rate

1.4s

p95 Latency

95%

Hit Rate

$0.006

Cost/Call

Violations

Calls / Success / Errors

Latency p50 / p95

Error Explorer

Error Code

Count

auth_error

102

schema_error

timeout

Query Shape & Cost

Fingerprint

Calls

Cost

p95 (ms)

a48lb03c530

420

$75.0k

1450

ad575e90036

260

$47.0k

980

Raw Logs

run_id

agent_id

tool_name

status

latency_ms

14:32:15

run_001

triage_bot

fetch_customer_context

success

2100

14:32:12

run_002

support_bot

fetch_customer_context

error

14:32:08

run_003

triage_bot

fetch_customer_context

success

1800

Track success rates, analyze errors, understand query patterns. Use Evals to refine views and tools without redeploying agents.

Making AI Agents Production-Ready

Don't let AI be your next security incident

Most agent connections run without guardrails. Pylar gives you control, visibility, and isolation.

Uncontrolled Agent Access

Everyday tools like Claude, Cursor, and ChatGPT can interact with live data without oversight.

Accidental Data Exposure

One misconfigured MCP server can leak PII, code, or source-of-truth logic instantly.

Blind Spots Everywhere

Without observability, agent access is invisible and unaudited.

Expanding Attack Surface

Agents talk to unvetted MCP servers, introducing unknown security vectors.

How Pylar keeps your data safe

Your agents see only what you allow—nothing more.

Credential Isolation

Credentials stored securely using cloud KMS. Agents never see secrets.

View-Level Governance

Agents query SQL views you define—never raw tables. Full control over rows, columns, and PII.

Safe Query Abstraction

MCP tools execute predefined SQL. No arbitrary queries, no overexposed data.

Zero Raw Database Access

Agents never interact with your warehouse directly. Pylar becomes the safe layer in-between.

Datasources

Unify your data stack. Connect warehouses, databases, and SaaS tools—then join them in a single query. Agents get unified context without you building custom integrations.

BigQuery

Enterprise data warehouse for fast analytics.

Redshift

AWS cloud data warehouse optimized for scale.

Snowflake

Cloud-native data platform for unified analytics.

MotherDuck

Serverless DuckDB-based analytics for small teams.

Supabase

Open-source Postgres backend for modern apps.

MySQL

The world's most popular open-source database.

PostgreSQL

Advanced open-source relational database system.

MS SQL Server

Microsoft's trusted relational data platform.

Agent Builders

Framework-agnostic by design. One MCP server URL works with LangGraph, Claude Desktop, Zapier, n8n, and every agent builder. Your governance policies travel with the data, regardless of which framework your teams choose.

OpenAI

Deploy MCP tools directly to GPT-4, GPT-4 Turbo, and custom assistants.

Claude

Connect your Pylar tools to Anthropic's Claude models via MCP.

Cursor

AI-powered code editor with MCP support for seamless Pylar integration.

Windsurf

AI coding assistant that supports MCP tools for data access.

VS Code

Connect Pylar tools to Visual Studio Code with MCP extension support.

LangGraph

Integrate Pylar views and tools into LangGraph workflows and agents.

n8n

Automate workflows with Pylar data connections in n8n pipelines.

Zapier

Connect Pylar to thousands of apps through Zapier integrations.

Make

Build powerful automations with Pylar data in Make workflows.

Agno

Deploy Pylar tools to Agno's agent platform for custom AI solutions.

Quick start

From data view to production agent tool in under 2 minutes. No backend engineering, no API endpoints, no deployment pipeline. Just SQL and natural language.

View all docs →

The simplest, safest way to connect agents to your data stack

Trusted by teams shipping production AI

How Pylar Works

Don't let AI be your next security incident

Uncontrolled Agent Access

Accidental Data Exposure

Blind Spots Everywhere

Expanding Attack Surface

How Pylar keeps your data safe

Credential Isolation

View-Level Governance

Safe Query Abstraction

Zero Raw Database Access

Datasources

BigQuery

Redshift

Snowflake

MotherDuck

Supabase

MySQL

PostgreSQL

MS SQL Server

Agent Builders

OpenAI

Claude

Cursor

Windsurf

VS Code

LangGraph

n8n

Zapier

Make

Agno

Quick start

Creating Data Views

Building MCP Tools

Connecting Agent Builders