Kubit MCP Server

Warehouse-native analytics meets conversational AI

Bring the full power of Kubit directly into your AI workflow. Query, analyze, and explore your data warehouse through natural language—no complex syntax required.

What is Kubit MCP?

The Kubit MCP (Model Context Protocol) server transforms how teams interact with their analytics platform. By connecting your AI assistant to Kubit, you can:

• Explore schemas - Discover events, properties, and dimensions in natural language
• Generate reports - Create analytical queries through conversation
• Export data - Pull raw data in CSV format for deep analysis
• Search content - Find existing reports and dashboards instantly
• Ask questions - Get insights without learning query syntax

Beta Notice

This server is under active development. You may encounter bugs, performance issues, or rate limits as we continue to improve the platform.

Quick Start

What You'll Need

Connection Steps

Setting up the Kubit MCP server is straightforward:

1. Add the MCP server to your AI client configuration
2. Use the server URL: https://mcp.kubit.ai/mcp
3. Complete OAuth authentication when prompted
4. Start querying your Kubit data

Note: Check your AI client's documentation for specific MCP server setup instructions.

Authentication & Access

The server uses OAuth 2.0 authentication and respects your existing Kubit permissions. You'll only see data from schemas you already have access to—no additional permissions needed.

Tools & Capabilities

Your AI assistant gains access to five powerful tools:

Example Conversations

Understanding User Behavior

"Show me conversion funnel for mobile app sign-ups in the last quarter"
"What are the most popular features used by premium users?"
"How has user retention changed month-over-month?"

Product Performance

"What are the top events by volume this week?"
"Show me user engagement trends for the last 30 days"
"Compare conversion rates across different traffic sources"

Data Discovery

"What events and properties are available in the mobile app schema?"
"Show me all custom properties for the checkout event"
"What dimensions can I use for user segmentation?"

Typical Workflow

Here's how most analysis sessions flow:

Initialize → Explore → Search → Create → Export

1. Initialize - Call getUserContext to see available schemas
2. Explore - Use getSchema to understand events and properties
3. Search - Check searchKubit for existing analyses
4. Create - Generate new reports with custom queries
5. Export - Pull getRawData for external analysis

Best Practices

Crafting Effective Prompts

Be Specific
Include time ranges, events, and segments in your questions.

- "Show me users"
+ "Show me active users in the US who signed up last month"

Provide Context
Explain what you're trying to understand.

- "What's the conversion rate?"
+ "What's the conversion rate from free trial to paid for users who engaged with feature X?"

Reference Schemas
Use schema names when working with multiple data sources.

- "Show me sign-up events"
+ "In the mobile_events schema, show me sign-up events"

... View full README on GitHub