How to Use the Google BigQuery MCP in LlamaIndex

Q: Can LlamaIndex search through my warehouse schemas?

Yes, the agent can use listtables and gettable to pull metadata. It indexes these schemas into your vector store, allowing users to find tables using natural language.

Q: How do I track slow analytical queries in Google BigQuery with LlamaIndex?

Your agent can call listjobs to view recent query executions. If a query is still running, the agent uses getjob to monitor its progress before indexing the output.

Index live analytical data directly from your warehouse into your LlamaIndex vector store using this MCP Server.

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Connect Google BigQuery MCP to LlamaIndex

Create your Vinkius account to connect Google BigQuery to LlamaIndex and route execution through our secure gateway. The platform manages server hosting, runtime updates, and security layers. Configuration requires no manual server provisioning.

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Setup Google BigQuery with LlamaIndex

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Grounding RAG in Live Warehouse Data

The `execute_query` tool pulls fresh analytical records directly from your database to ground your index in real-time facts. LlamaIndex ingestion pipelines can run this tool to update vector stores with the latest business metrics. This workflow eliminates hallucinations by feeding your agent actual numbers instead of stale training data. Your search queries return answers backed by active warehouse records.

Automated Metadata Indexing

The `list_datasets` tool allows your LlamaIndex agent to scan your entire GCP project structure. It maps out your data hierarchy automatically, indexing table descriptions and schemas for semantic search. By combining this with `list_tables`, your agent builds a searchable map of your data warehouse. Users can ask where specific metrics live, and the agent finds the exact table.

Schema-Aware Query Generation with MCP Server

The `get_table` tool fetches the exact column definitions so your agent knows the data types before querying. This step prevents the syntax errors that usually happen when LLMs guess schema layouts. Once the agent has the schema, it uses `get_dataset` to understand the broader context of the tables. This MCP Server integration results in highly accurate SQL generation that runs successfully on the first try.

Setup guide

Set up Google BigQuery MCP in LlamaIndex

Prerequisites

Python 3.10+ installed
llama-index-tools-mcp package
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install llama-index-tools-mcp llama-index-llms-openai. The MCP tools package provides BasicMCPClient and McpToolSpec.
2

Connect with BasicMCPClient
Point BasicMCPClient to your Vinkius endpoint URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. Supports SSE and Streamable HTTP transports.
3

Convert to LlamaIndex tools
Call mcp_tool_spec.to_tool_list_async() to convert all Google BigQuery MCP tools into native FunctionTool objects that any LlamaIndex agent can use.
4

Run with any LLM
Create a FunctionAgent with the tools and your preferred LLM. Swap OpenAI for Anthropic, Gemini, or any LlamaIndex-supported provider.

agent.py

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

# Connect to the MCP
mcp_client = BasicMCPClient(
    "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)
mcp_tool_spec = McpToolSpec(client=mcp_client)

# Convert MCP tools to LlamaIndex tools
tools = await mcp_tool_spec.to_tool_list_async()

# Create and run the agent
agent = FunctionAgent(
    tools=tools,
    llm=OpenAI(model="gpt-4o"),
    system_prompt="You have access to Google BigQuery tools.",
)
response = await agent.run("List recent Google BigQuery data")

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Common questions about Google BigQuery MCP in LlamaIndex

You initialize the MCP client with your Vinkius endpoint and pass the tools to your LlamaIndex agent. The agent then uses `execute_query` to fetch data and index the results.

Yes, the agent can use `list_tables` and `get_table` to pull metadata. It indexes these schemas into your vector store, allowing users to find tables using natural language.

Your agent can call `list_jobs` to view recent query executions. If a query is still running, the agent uses `get_job` to monitor its progress before indexing the output.

You avoid installing heavy Google Cloud SDKs in your local environment. Vinkius provides a single endpoint that exposes all seven tools securely to your LlamaIndex pipeline.

Schema metadata and query results are processed entirely within ephemeral, zero-trust sandboxes. Vinkius never stores your table definitions or query payloads on persistent disks.

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