Looker (Business Intelligence & Data) MCP Server for LlamaIndex 7 tools — connect in under 2 minutes
LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add Looker (Business Intelligence & Data) as an MCP tool provider through the Vinkius and your agents can query, analyze, and act on live data alongside your existing indexes.
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Vinkius supports streamable HTTP and SSE.
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 Looker (Business Intelligence & Data). "
"You have 7 tools available."
),
)
response = await agent.run(
"What tools are available in Looker (Business Intelligence & Data)?"
)
print(response)
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.
LlamaIndex agents combine Looker (Business Intelligence & Data) tool responses with indexed documents for comprehensive, grounded answers. Connect 7 tools through the 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
- 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 LlamaIndex 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 LlamaIndex via MCP
Follow these steps to integrate the Looker (Business Intelligence & Data) MCP Server with LlamaIndex.
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 7 tools from Looker (Business Intelligence & Data)
Why Use LlamaIndex with the Looker (Business Intelligence & Data) MCP Server
LlamaIndex provides unique advantages when paired with Looker (Business Intelligence & Data) through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine Looker (Business Intelligence & Data) tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain Looker (Business Intelligence & Data) tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query Looker (Business Intelligence & Data), a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what Looker (Business Intelligence & Data) tools were called, what data was returned, and how it influenced the final answer
Looker (Business Intelligence & Data) + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the Looker (Business Intelligence & Data) MCP Server delivers measurable value.
Hybrid search: combine Looker (Business Intelligence & Data) real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query Looker (Business Intelligence & Data) 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 Looker (Business Intelligence & Data) for fresh data
Analytical workflows: chain Looker (Business Intelligence & Data) queries with LlamaIndex's data connectors to build multi-source analytical reports
Looker (Business Intelligence & Data) MCP Tools for LlamaIndex (7)
These 7 tools become available when you connect Looker (Business Intelligence & Data) to LlamaIndex 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 LlamaIndex
Ready-to-use prompts you can give your LlamaIndex 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 LlamaIndex
Common issues when connecting Looker (Business Intelligence & Data) to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpLooker (Business Intelligence & Data) + LlamaIndex FAQ
Common questions about integrating Looker (Business Intelligence & Data) MCP Server with LlamaIndex.
How does LlamaIndex connect to MCP servers?
Can I combine MCP tools with vector stores?
Does LlamaIndex support async MCP calls?
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 LlamaIndex
Get your token, paste the configuration, and start using 7 tools in under 2 minutes. No API key management needed.