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

Q: How do I discover specific BI reports using Looker (Business Intelligence & Data) in LangChain?

Use searchcontent inside a search chain to locate the report. Your LangChain agent can then use the returned ID to pull the full data payload using getlook.

Fetch Looker dimensions and run ad-hoc queries directly inside your LangChain reasoning loops.

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Connect Looker (Business Intelligence & Data) MCP to LangChain

Create your Vinkius account to connect Looker (Business Intelligence & Data) to LangChain 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 Looker (Business Intelligence & Data) with LangChain

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Run Looker inline queries within LangChain loops

Let your LangChain agents run live analytical queries instead of relying on stale database dumps. By calling `run_inline_query`, your agent constructs looker-specific queries on the fly, fetching dimensions and measures based on the user's natural language request. The output feeds directly into the next step of your chain. You can pipe raw JSON results from Looker into a summarizer or a plotting tool without writing glue code to parse the BI schema.

Audit your BI assets with this MCP Server

Keep track of your reporting sprawl by letting your chain inspect dashboard layouts. Your agent uses `list_dashboards` to find target assets, then pulls down exact widget configurations via `get_dashboard` to analyze user engagement or broken references. This setup lets you build automated Slack alerts when specific Looker dashboard components change. LangChain handles the decision logic, while this MCP Server handles the data extraction.

Crawl Looker folders recursively

Stop guessing where your metrics live. Your chain can trigger `list_folders` and `search_content` to map out the entire Looker instance, finding the exact Looks or Dashboards your team needs. Because LangChain traces every step, you can see exactly which folder path was traversed before your agent settled on a specific `get_look` call. It makes debugging complex BI discovery chains painless.

Setup guide

Set up Looker (Business Intelligence & Data) MCP in LangChain

Prerequisites

Python 3.10+ installed
langchain-mcp-adapters + langgraph packages
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install langchain-mcp-adapters langgraph langchain-openai. The MCP adapters package converts MCP tools into native LangChain BaseTool objects.
2

Connect via HTTP transport
Use MultiServerMCPClient with "transport": "http" pointing to your Vinkius endpoint. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.
3

Create a ReAct agent
Pass the discovered tools to create_react_agent() from LangGraph. The agent automatically routes Looker (Business Intelligence & Data) tool calls through the MCP protocol.
4

Run with any LLM
Swap ChatOpenAI for ChatAnthropic, ChatGoogleGenerativeAI, or any LangChain-compatible model. The MCP tools work identically across all providers.

agent.py

from langchain_mcp_adapters.client import MultiServerMCPClient
from langgraph.prebuilt import create_react_agent
from langchain_openai import ChatOpenAI

async with MultiServerMCPClient({
    "looker-business-intelligence-data-mcp": {
        "transport": "http",
        "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp",
    }
}) as client:
    tools = client.get_tools()

    agent = create_react_agent(
        ChatOpenAI(model="gpt-4o"),
        tools,
    )
    result = await agent.ainvoke({
        "messages": "List recent Looker (Business Intelligence & Data) transactions"
    })
    print(result["messages"][-1].content)

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

Pass the tools directly to your agent initialization using the LangChain adapter. The agent will automatically call `run_inline_query` with the correct dimensions when a user asks for live metrics.

You should manage rate limits at the Looker API level or wrap your LangChain tool calls in a custom run manager. The server passes the raw Looker SDK errors straight to your chain so you can handle retries.

Use `search_content` inside a search chain to locate the report. Your LangChain agent can then use the returned ID to pull the full data payload using `get_look`.

It runs over standard transport protocols like HTTP or stdio. You configure it via the MultiServerMCPClient, which exposes all Looker tools to your LangChain environment.

Yes, because your Looker credentials and query results stay within your local environment or secure VPC. This MCP Server only acts as a bridge, passing dashboard metadata and raw inline query outputs directly to your execution chain without external storage.

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