Looker (Business Intelligence & Data) MCP Server for LangChain 7 tools — connect in under 2 minutes
LangChain is the leading Python framework for composable LLM applications. Connect Looker (Business Intelligence & Data) through the Vinkius and LangChain agents can call every tool natively — combine them with retrievers, memory, and output parsers for sophisticated AI pipelines.
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Vinkius supports streamable HTTP and SSE.
import asyncio
from langchain_mcp_adapters.client import MultiServerMCPClient
from langchain_openai import ChatOpenAI
from langgraph.prebuilt import create_react_agent
async def main():
# Your Vinkius token — get it at cloud.vinkius.com
async with MultiServerMCPClient({
"looker-business-intelligence-data": {
"transport": "streamable_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,
)
response = await agent.ainvoke({
"messages": [{
"role": "user",
"content": "Using Looker (Business Intelligence & Data), show me what tools are available.",
}]
})
print(response["messages"][-1].content)
asyncio.run(main())
* Every MCP server runs on Vinkius-managed infrastructure inside AWS - a purpose-built runtime with per-request V8 isolates, Ed25519 signed audit chains, and sub-40ms cold starts optimized for native MCP execution. See our infrastructure
About Looker (Business Intelligence & Data) MCP Server
Connect your Looker instance to any AI agent and take full control of your enterprise business intelligence and data analytics through natural conversation.
LangChain's ecosystem of 500+ components combines seamlessly with Looker (Business Intelligence & Data) through native MCP adapters. Connect 7 tools via the Vinkius and use ReAct agents, Plan-and-Execute strategies, or custom agent architectures — with LangSmith tracing giving full visibility into every tool call, latency, and token cost.
What you can do
- Dashboard Orchestration — List all managed dashboards and retrieve detailed configuration metrics and query structures directly from your agent
- Dynamic Data Queries — Execute inline queries against specific models and views to fetch literal dimensions and measures in real-time
- Look & Report Audit — Access saved 'Looks' to retrieve model mappings and applied filters for consistent data reporting across your organization
- Content & Folder Search — Search through content metadata and navigate folder hierarchies to identify key datasets and analytical assets securely
- Metadata Inspection — Extract precise UUIDs and configuration trees for dashboards and looks to understand the underlying data logic
- Resource Inventory — Enumerate root folders and top-level models to audit permissions and organizational structure across your Looker tenant
The Looker (Business Intelligence & Data) MCP Server exposes 7 tools through the Vinkius. Connect it to LangChain in under two minutes — no API keys to rotate, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.
How to Connect Looker (Business Intelligence & Data) to LangChain via MCP
Follow these steps to integrate the Looker (Business Intelligence & Data) MCP Server with LangChain.
Install dependencies
Run pip install langchain langchain-mcp-adapters langgraph langchain-openai
Replace the token
Replace [YOUR_TOKEN_HERE] with your Vinkius token
Run the agent
Save the code and run python agent.py
Explore tools
The agent discovers 7 tools from Looker (Business Intelligence & Data) via MCP
Why Use LangChain with the Looker (Business Intelligence & Data) MCP Server
LangChain provides unique advantages when paired with Looker (Business Intelligence & Data) through the Model Context Protocol.
The largest ecosystem of integrations, chains, and agents — combine Looker (Business Intelligence & Data) MCP tools with 500+ LangChain components
Agent architecture supports ReAct, Plan-and-Execute, and custom strategies with full MCP tool access at every step
LangSmith tracing gives you complete visibility into tool calls, latencies, and token usage for production debugging
Memory and conversation persistence let agents maintain context across Looker (Business Intelligence & Data) queries for multi-turn workflows
Looker (Business Intelligence & Data) + LangChain Use Cases
Practical scenarios where LangChain combined with the Looker (Business Intelligence & Data) MCP Server delivers measurable value.
RAG with live data: combine Looker (Business Intelligence & Data) tool results with vector store retrievals for answers grounded in both real-time and historical data
Autonomous research agents: LangChain agents query Looker (Business Intelligence & Data), synthesize findings, and generate comprehensive research reports
Multi-tool orchestration: chain Looker (Business Intelligence & Data) tools with web scrapers, databases, and calculators in a single agent run
Production monitoring: use LangSmith to trace every Looker (Business Intelligence & Data) tool call, measure latency, and optimize your agent's performance
Looker (Business Intelligence & Data) MCP Tools for LangChain (7)
These 7 tools become available when you connect Looker (Business Intelligence & Data) to LangChain via MCP:
get_dashboard
Get complete details and queries mapping a Looker Dashboard ID
get_look
Get full mapped details tracing a strict Looker target Look object
list_dashboards
List Looker dashboards
list_folders
List root Folders analyzing explicit environment structures
list_looks
List saved specific dataset mappings tracked as Looks
run_inline_query
Execute queries building models specifically fetching literal dimensions dynamically natively
search_content
Search content metadata explicit mapping targets natively across instance
Example Prompts for Looker (Business Intelligence & Data) in LangChain
Ready-to-use prompts you can give your LangChain agent to start working with Looker (Business Intelligence & Data) immediately.
"List the last 5 dashboards created in my Looker instance"
"Run a query using model 'sales' and view 'orders' for fields 'orders.created_date' and 'orders.total_amount'"
"Find all dashboards related to 'Marketing ROI'"
Troubleshooting Looker (Business Intelligence & Data) MCP Server with LangChain
Common issues when connecting Looker (Business Intelligence & Data) to LangChain through the Vinkius, and how to resolve them.
MultiServerMCPClient not found
pip install langchain-mcp-adaptersLooker (Business Intelligence & Data) + LangChain FAQ
Common questions about integrating Looker (Business Intelligence & Data) MCP Server with LangChain.
How does LangChain connect to MCP servers?
langchain-mcp-adapters to create an MCP client. LangChain discovers all tools and wraps them as native LangChain tools compatible with any agent type.Which LangChain agent types work with MCP?
Can I trace MCP tool calls in LangSmith?
Connect Looker (Business Intelligence & Data) with your favorite client
Step-by-step setup guides for every MCP-compatible client and framework:
Anthropic's native desktop app for Claude with built-in MCP support.
AI-first code editor with integrated LLM-powered coding assistance.
GitHub Copilot in VS Code with Agent mode and MCP support.
Purpose-built IDE for agentic AI coding workflows.
Autonomous AI coding agent that runs inside VS Code.
Anthropic's agentic CLI for terminal-first development.
Python SDK for building production-grade OpenAI agent workflows.
Google's framework for building production AI agents.
Type-safe agent development for Python with first-class MCP support.
TypeScript toolkit for building AI-powered web applications.
TypeScript-native agent framework for modern web stacks.
Python framework for orchestrating collaborative AI agent crews.
Leading Python framework for composable LLM applications.
Data-aware AI agent framework for structured and unstructured sources.
Microsoft's framework for multi-agent collaborative conversations.
Connect Looker (Business Intelligence & Data) to LangChain
Get your token, paste the configuration, and start using 7 tools in under 2 minutes. No API key management needed.