Cube.dev MCP Server for OpenAI Agents SDKGive OpenAI Agents SDK instant access to 15 tools to Check Live, Check Ready, Convert Query, and more

Name: Cube.dev
Price: 29 USD
Availability: InStock
Rating: 0.2 (1 reviews)
Author: Vinkius

GDPR Free for Subscribers

The OpenAI Agents SDK enables production-grade agent workflows in Python. Connect Cube.dev through Vinkius and your agents gain typed, auto-discovered tools with built-in guardrails. no manual schema definitions required.

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The Cube.dev MCP Server for OpenAI Agents SDK is a standout in the Brain Trust category — giving your AI agent 15 tools to work with, ready to go from day one.

Built for AI Agents by Vinkius

Vinkius delivers Streamable HTTP and SSE to any MCP client

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+ other MCP clients

python

import asyncio
from agents import Agent, Runner
from agents.mcp import MCPServerStreamableHttp

async def main():
    # Your Vinkius token. get it at cloud.vinkius.com
    async with MCPServerStreamableHttp(
        url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
    ) as mcp_server:

        agent = Agent(
            name="Cube.dev Assistant",
            instructions=(
                "You help users interact with Cube.dev. "
                "You have access to 15 tools."
            ),
            mcp_servers=[mcp_server],
        )

        result = await Runner.run(
            agent, "List all available tools from Cube.dev"
        )
        print(result.final_output)

asyncio.run(main())

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About Cube.dev MCP Server

Connect your Cube.dev instance to any AI agent to bridge the gap between natural language and your data warehouse. This server allows your agent to interact with Cube's semantic layer, ensuring consistent metrics and high-performance data retrieval.

The OpenAI Agents SDK auto-discovers all 15 tools from Cube.dev through native MCP integration. Build agents with built-in guardrails, tracing, and handoff patterns. chain multiple agents where one queries Cube.dev, another analyzes results, and a third generates reports, all orchestrated through Vinkius.

What you can do

Data Querying — Execute complex REST API queries using load_query to fetch aggregated data with measures, dimensions, and filters.
SQL Inspection — Use get_sql and execute_cube_sql to debug or run raw queries against the SQL API for deep data investigation.
Metadata Exploration — Retrieve cube definitions, views, and segments via get_meta to understand your data model without leaving the chat.
Performance Management — Trigger and monitor background pre-aggregation builds with trigger_pre_aggregation_job to ensure your dashboards stay fast.
Cloud Management — List deployments and environments if using Cube Cloud to manage your infrastructure context.

trigger

Trigger pre aggregation job on Cube.dev

Trigger a pre-aggregation build job

Connect Cube.dev to OpenAI Agents SDK via MCP

Follow these steps to wire Cube.dev into OpenAI Agents SDK. The entire setup takes under two minutes — your credentials stay safe behind Vinkius.

Install the SDK

Run pip install openai-agents in your Python environment

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token from cloud.vinkius.com

Run the script

Save the code above and run it: python agent.py

Explore tools

The agent will automatically discover 15 tools from Cube.dev

Why Use OpenAI Agents SDK with the Cube.dev MCP Server

OpenAI Agents SDK provides unique advantages when paired with Cube.dev through the Model Context Protocol.

Native MCP integration via `MCPServerSse`, pass the URL and the SDK auto-discovers all tools with full type safety

Built-in guardrails, tracing, and handoff patterns let you build production-grade agents without reinventing safety infrastructure

Lightweight and composable: chain multiple agents and MCP servers in a single pipeline with minimal boilerplate

First-party OpenAI support ensures optimal compatibility with GPT models for tool calling and structured output

Cube.dev + OpenAI Agents SDK Use Cases

Practical scenarios where OpenAI Agents SDK combined with the Cube.dev MCP Server delivers measurable value.

Automated workflows: build agents that query Cube.dev, process the data, and trigger follow-up actions autonomously

Multi-agent orchestration: create specialist agents. one queries Cube.dev, another analyzes results, a third generates reports

Data enrichment pipelines: stream data through Cube.dev tools and transform it with OpenAI models in a single async loop

Customer support bots: agents query Cube.dev to resolve tickets, look up records, and update statuses without human intervention

Example Prompts for Cube.dev in OpenAI Agents SDK

Ready-to-use prompts you can give your OpenAI Agents SDK agent to start working with Cube.dev immediately.

"Show me the metadata for all available cubes and views."

"Run a query to get the total count of orders grouped by status for the last 30 days."

"Trigger a pre-aggregation build for the 'Sales' cube."

Troubleshooting Cube.dev MCP Server with OpenAI Agents SDK

Common issues when connecting Cube.dev to OpenAI Agents SDK through Vinkius, and how to resolve them.

MCPServerStreamableHttp not found

Ensure you have the latest version: pip install --upgrade openai-agents

Agent not calling tools

Make sure your prompt explicitly references the task the tools can help with.

Cube.dev + OpenAI Agents SDK FAQ

Common questions about integrating Cube.dev MCP Server with OpenAI Agents SDK.

How does the OpenAI Agents SDK connect to MCP?

Use MCPServerSse(url=...) to create a server connection. The SDK auto-discovers all tools and makes them available to your agent with full type information.

Can I use multiple MCP servers in one agent?

Yes. Pass a list of MCPServerSse instances to the agent constructor. The agent can use tools from all connected servers within a single run.

Does the SDK support streaming responses?

Yes. The SDK supports SSE and Streamable HTTP transports, both of which work natively with Vinkius.

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