Cube.dev MCP Server for LlamaIndexGive LlamaIndex 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

LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add Cube.dev as an MCP tool provider through Vinkius and your agents can query, analyze, and act on live data alongside your existing indexes.

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The Cube.dev MCP Server for LlamaIndex 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

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python

import asyncio
from llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI

async def main():
    # Your Vinkius token. get it at cloud.vinkius.com
    mcp_client = BasicMCPClient("https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")
    mcp_tool_spec = McpToolSpec(client=mcp_client)
    tools = await mcp_tool_spec.to_tool_list_async()

    agent = FunctionAgent(
        tools=tools,
        llm=OpenAI(model="gpt-4o"),
        system_prompt=(
            "You are an assistant with access to Cube.dev. "
            "You have 15 tools available."
        ),
    )

    response = await agent.run(
        "What tools are available in Cube.dev?"
    )
    print(response)

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.

LlamaIndex agents combine Cube.dev tool responses with indexed documents for comprehensive, grounded answers. Connect 15 tools through Vinkius and query live data alongside vector stores and SQL databases in a single turn. ideal for hybrid search, data enrichment, and analytical workflows.

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 LlamaIndex via MCP

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

Install dependencies

Run pip install llama-index-tools-mcp llama-index-llms-openai

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token

Run the agent

Save to agent.py and run: python agent.py

Explore tools

The agent discovers 15 tools from Cube.dev

Why Use LlamaIndex with the Cube.dev MCP Server

LlamaIndex provides unique advantages when paired with Cube.dev through the Model Context Protocol.

Data-first architecture: LlamaIndex agents combine Cube.dev tool responses with indexed documents for comprehensive, grounded answers

Query pipeline framework lets you chain Cube.dev tool calls with transformations, filters, and re-rankers in a typed pipeline

Multi-source reasoning: agents can query Cube.dev, a vector store, and a SQL database in a single turn and synthesize results

Observability integrations show exactly what Cube.dev tools were called, what data was returned, and how it influenced the final answer

Cube.dev + LlamaIndex Use Cases

Practical scenarios where LlamaIndex combined with the Cube.dev MCP Server delivers measurable value.

Hybrid search: combine Cube.dev real-time data with embedded document indexes for answers that are both current and comprehensive

Data enrichment: query Cube.dev to augment indexed data with live information before generating user-facing responses

Knowledge base agents: build agents that maintain and update knowledge bases by periodically querying Cube.dev for fresh data

Analytical workflows: chain Cube.dev queries with LlamaIndex's data connectors to build multi-source analytical reports

Example Prompts for Cube.dev in LlamaIndex

Ready-to-use prompts you can give your LlamaIndex 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 LlamaIndex

Common issues when connecting Cube.dev to LlamaIndex through Vinkius, and how to resolve them.

BasicMCPClient not found

Install: pip install llama-index-tools-mcp

Cube.dev + LlamaIndex FAQ

Common questions about integrating Cube.dev MCP Server with LlamaIndex.

How does LlamaIndex connect to MCP servers?

Use the MCP client adapter to create a connection. LlamaIndex discovers all tools and wraps them as query engine tools compatible with any LlamaIndex agent.

Can I combine MCP tools with vector stores?

Yes. LlamaIndex agents can query Cube.dev tools and vector store indexes in the same turn, combining real-time and embedded data for grounded responses.