MCP Server (AI-Ready Data Layer)

Drizzle Cube includes a built-in MCP server that lets AI agents like Claude, ChatGPT, and n8n query your semantic layer directly. The server follows the Model Context Protocol specification and exposes tools for discovering cubes, validating queries, and executing them.

MCP Server Endpoint

All framework adapters expose an MCP server at /mcp:

https://your-app.com/mcp

This endpoint implements the full MCP specification including:

• Tools - Functions the AI can call
• Prompts - Pre-built prompts for common tasks
• Server-Sent Events (SSE) - For streaming responses

Available MCP Tools

Tool	Purpose
drizzle_cube_discover	Find relevant cubes based on topic or intent
drizzle_cube_validate	Validate queries and get auto-corrections
drizzle_cube_load	Execute queries and return results

Connecting AI Tools

Claude Desktop

Add to your claude_desktop_config.json:

{
  "mcpServers": {
    "your-app-analytics": {
      "command": "npx",
      "args": ["-y", "@anthropic/mcp-remote", "https://your-app.com/mcp"]
    }
  }
}

Claude.ai (Web)

1. Go to Settings → Connectors → Add Connector
2. Enter your MCP server URL: https://your-app.com/mcp
3. The tools will be available in your conversations

ChatGPT

1. Go to Settings → Connectors → Advanced → Developer Mode
2. Add your MCP server URL: https://your-app.com/mcp
3. The tools will be available in ChatGPT

n8n

Use the MCP Client node:

1. Add an MCP Client node to your workflow
2. Set the server URL: https://your-app.com/mcp
3. Connect it to an AI Agent node

See n8n MCP Client documentation for details.

The AI Workflow

When an AI agent connects to your MCP server, it typically follows this workflow:

User: "Show me average salary by department"
         │
         ▼
┌─────────────────────────────────────────────────────┐
│  1. drizzle_cube_discover                           │
│     Find cubes related to "salary" and "department" │
│     → Returns: Employees, Departments cubes with    │
│       suggested measures and dimensions             │
└─────────────────────────────────────────────────────┘
         │
         ▼
┌─────────────────────────────────────────────────────┐
│  2. AI builds query using metadata                  │
│     The AI uses cube metadata to construct a query  │
│     → Query: { measures: ['Employees.avgSalary'],   │
│                dimensions: ['Departments.name'] }   │
└─────────────────────────────────────────────────────┘
         │
         ▼
┌─────────────────────────────────────────────────────┐
│  3. drizzle_cube_validate (optional)                │
│     Check query validity, get corrections if needed │
│     → Returns: { isValid: true, correctedQuery }    │
└─────────────────────────────────────────────────────┘
         │
         ▼
┌─────────────────────────────────────────────────────┐
│  4. drizzle_cube_load                               │
│     Execute query with security context             │
│     → Returns: { data: [...], annotation: {...} }   │
└─────────────────────────────────────────────────────┘

Tool Reference

drizzle_cube_discover

Find cubes relevant to a topic or intent.

// Parameters
{
  "topic": "salary",
  "intent": "I want to analyze compensation",
  "limit": 5,
  "minScore": 0.3
}

// Response
{
  "cubes": [
    {
      "cube": "Employees",
      "relevanceScore": 0.85,
      "matchedOn": ["measure:avgSalary", "measure:totalSalary"],
      "suggestedMeasures": ["Employees.avgSalary", "Employees.totalSalary"],
      "suggestedDimensions": ["Employees.department", "Employees.location"]
    }
  ]
}

drizzle_cube_validate

Validate a query and get helpful corrections.

// Parameters
{
  "query": {
    "measures": ["Employees.cont"],
    "dimensions": ["Departments.nam"]
  }
}

// Response
{
  "isValid": false,
  "errors": [
    "Unknown measure 'Employees.cont' - did you mean 'Employees.count'?",
    "Unknown dimension 'Departments.nam' - did you mean 'Departments.name'?"
  ],
  "correctedQuery": {
    "measures": ["Employees.count"],
    "dimensions": ["Departments.name"]
  }
}

drizzle_cube_load

Execute a query and return results.

// Parameters
{
  "query": {
    "measures": ["Employees.count", "Employees.avgSalary"],
    "dimensions": ["Departments.name"]
  }
}

// Response
{
  "data": [
    { "Departments.name": "Engineering", "Employees.count": 45, "Employees.avgSalary": 125000 },
    { "Departments.name": "Sales", "Employees.count": 32, "Employees.avgSalary": 85000 }
  ],
  "annotation": {
    "measures": {
      "Employees.count": { "title": "Total Employees", "type": "count" },
      "Employees.avgSalary": { "title": "Average Salary", "type": "avg" }
    },
    "dimensions": {
      "Departments.name": { "title": "Department Name", "type": "string" }
    }
  }
}

Configuration

Disabling MCP

If you don’t want the MCP server exposed:

createCubeRouter({
  // ... other options
  mcp: {
    enabled: false
  }
})

Selective Tool Exposure

Expose only specific MCP tools:

createCubeRouter({
  // ... other options
  mcp: {
    enabled: true,
    tools: ['discover', 'validate', 'load']  // Only expose these
  }
})

Origin Restrictions

Restrict which origins can connect to your MCP server:

createCubeRouter({
  // ... other options
  mcp: {
    enabled: true,
    allowedOrigins: ['https://claude.ai', 'https://chat.openai.com']
  }
})

Security & Authentication

Important: The MCP server does not include built-in authentication. You are responsible for adding authentication middleware, just like with the standard Cube API routes.

Authentication Is Your Responsibility

Apply authentication middleware before mounting the cube router:

import { createCubeRouter } from 'drizzle-cube/adapters/express'
import { authMiddleware } from './auth'

const app = express()

// Apply authentication BEFORE mounting the cube router
app.use(authMiddleware)

// Now both /cubejs-api/v1/* AND /mcp require authentication
const cubeRouter = createCubeRouter({
  cubes: [employeesCube],
  drizzle: db,
  schema,
  extractSecurityContext: async (req) => ({
    organisationId: req.user.orgId,
    userId: req.user.id
  })
})

app.use('/', cubeRouter)

Security Context Enforcement

All MCP tools respect your security context:

• drizzle_cube_load executes queries with the security context
• drizzle_cube_discover only returns cubes the user has access to
• Multi-tenant isolation is enforced on all data access

Enhancing AI Discovery

The MCP tools work best when your cubes have rich semantic metadata. See Adding Semantic Metadata for how to add:

• Descriptions for cubes, measures, and dimensions
• Synonyms for alternate names (“revenue” → “sales”, “income”)
• Example questions that help AI understand the cube’s purpose

Try It Live

Connect to the demo MCP server to try it out:

https://try.drizzle-cube.dev/mcp

Next Steps

• Adding Semantic Metadata - Make your cubes more discoverable
• Claude Desktop Setup - Connect Claude Desktop to your data
• Claude Code Plugin - Query from Claude Code