How to Use the Looker (Business Intelligence & Data) MCP in LlamaIndex

Q: Can I use Looker (Business Intelligence & Data) to ground LlamaIndex RAG pipelines?

Yes, you can configure your query engine to call runinlinequery when a user asks about dynamic metrics. This pulls live data into the context window instead of relying on static vector embeddings.

Q: How do I search for specific BI dashboards using Looker (Business Intelligence & Data) in LlamaIndex?

Your agent uses searchcontent to find the dashboard matching the user's intent. Once found, it retrieves the full dashboard structure using getdashboard.

Index Looker metadata and query results into your LlamaIndex vector stores for grounded RAG applications.

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Connect Looker (Business Intelligence & Data) MCP to LlamaIndex

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Setup Looker (Business Intelligence & Data) with LlamaIndex

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Index Looker metadata directly into LlamaIndex

Turn your BI catalog into a searchable knowledge base. By calling `list_dashboards` and `list_looks`, LlamaIndex ingests your Looker asset directory and indexes it into a vector store for quick semantic retrieval. When users ask questions about your business metrics, the system searches the index to find the exact dashboard ID. It stops your agent from guessing which report has the right numbers.

Ground your responses with live Looker MCP Server data

Avoid hallucinations by forcing your agent to fetch real numbers. Your LlamaIndex pipeline uses `run_inline_query` to pull fresh dimensions directly from your Looker models before answering user prompts. The raw data is fed into the context window, ensuring your agent writes reports based on actual database facts. It combines static document retrieval with live BI execution.

Map folder structures for context-aware search

Structure your index based on how your company organizes its data. The agent uses `list_folders` to understand your Looker hierarchy, keeping marketing metrics separated from finance metrics. By mapping these boundaries, LlamaIndex targets its `search_content` calls to specific folders, speeding up retrieval times and keeping results relevant.

Setup guide

Set up Looker (Business Intelligence & Data) 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 Looker (Business Intelligence & Data) 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 Looker (Business Intelligence & Data) tools.",
)
response = await agent.run("List recent Looker (Business Intelligence & Data) data")

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Common questions about Looker (Business Intelligence & Data) MCP in LlamaIndex

You load the tools using the LlamaIndex MCP tool spec and run them to fetch data. The outputs of `list_looks` or `list_dashboards` are converted into Document objects for indexing.

Yes, you can configure your query engine to call `run_inline_query` when a user asks about dynamic metrics. This pulls live data into the context window instead of relying on static vector embeddings.

Your agent uses `search_content` to find the dashboard matching the user's intent. Once found, it retrieves the full dashboard structure using `get_dashboard`.

Yes, you can use the asynchronous tool list method in this MCP Server to fetch metadata without blocking. This is useful when loading large folder trees via `list_folders`.

Your Looker API credentials and query results are processed entirely within your private MCP Server sandbox. No BI data or dashboard schemas are ever sent to external servers during indexing.

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