Wren Engine MCP Server: Enterprise Data Access That Actually Understands Your Business

Stop fighting with raw database connections in your AI agents. When you're building enterprise AI workflows, connecting to PostgreSQL or BigQuery is just the beginning—the real challenge is getting your AI to understand what "active customer" or "monthly recurring revenue" actually means in your business context.

The Enterprise Data Problem Nobody Talks About

You've probably built MCP servers that connect to databases before. The connection works fine, your AI can execute SQL queries, but then reality hits:

• Your agent calculates "customer churn" differently than your finance team
• It can't distinguish between "gross revenue" and "net revenue"
• Business stakeholders lose trust because the numbers don't match their reports
• You spend more time debugging semantic misunderstandings than building features

Raw database access gives you data. Wren Engine gives you business intelligence.

What Makes This Different

Wren Engine isn't just another database MCP server—it's a semantic layer that sits between your AI agents and enterprise data systems. Think of it as the translation layer that converts business intent into accurate, governed data operations.

Here's what you get:

Business Context Built-In: Define metrics like "active customers" or "monthly churn rate" once, and every AI interaction uses the same calculation. No more inconsistent results across different prompts.

Multi-Source Intelligence: Connect to Snowflake, BigQuery, PostgreSQL, and 8+ other data sources simultaneously. Your AI can join data across systems without you writing complex federation logic.

Governance by Default: Role-based access control and business definitions ensure your AI agents respect the same data governance rules as your BI dashboards.

Real-World Implementation

Instead of this typical MCP database interaction:

-- AI generates this, but is "active" 30 days or 90 days?
SELECT COUNT(*) FROM customers WHERE last_login > NOW() - INTERVAL '30 days'

You get semantic queries that understand your business:

# AI can now ask: "How many active customers do we have?"
# Wren Engine knows your business definition of "active"
result = await wren_query("active_customers", context="current_month")

Perfect For These Use Cases

AI-Powered BI Dashboards: Build agents that generate reports using your exact business definitions. Finance gets the same "revenue" calculation whether they ask the AI or check the dashboard.

Cross-Team Data Agents: Customer success asks about "churn risk" while sales asks about "qualified leads"—both get consistent, accurate data based on your established business rules.

Compliance and Audit Workflows: Ensure AI-generated reports meet the same data governance standards as your official business reporting.

Getting Started

The setup is straightforward if you're already working with enterprise data:

1. Install and run the engine:

git clone https://github.com/Canner/wren-engine.git
cd wren-engine
./gradlew clean build -x test
java -jar build/libs/wren-engine-*.jar server

2. Connect your data sources via config:

warehouse:
  type: snowflake  # or bigquery, postgres, etc.
  url: jdbc:snowflake://your-account.snowflakecomputing.com
  user: YOUR_USER
  password: YOUR_PASSWORD

3. Define your business semantics by posting your model definitions to /v1/models

4. Connect to your MCP client and start querying with business context

Integration with Your Workflow

Wren Engine slots directly into your existing MCP setup. If you're already using Claude, Cursor, or other MCP clients, you add this as another server in your configuration. The difference is that now your AI understands your business, not just your database schema.

Your development workflow becomes:

1. Define business metrics and calculations once in Wren
2. Build AI agents that use these semantic definitions
3. Deploy with confidence that your AI speaks the same business language as your stakeholders

Why This Matters Now

As AI agents become more central to business operations, the gap between raw data access and business understanding becomes a critical bottleneck. Wren Engine solves this by bringing enterprise-grade semantic layers into the MCP ecosystem.

You're not just connecting to databases—you're connecting to business intelligence. And that's exactly what enterprise AI agents need to be truly useful.

Ready to try it? Check out the GitHub repository and see how semantic data access changes your AI development workflow.