Looker (Business Intelligence & Data) MCP Server for Pydantic AI 7 tools — connect in under 2 minutes
Pydantic AI brings type-safe agent development to Python with first-class MCP support. Connect Looker (Business Intelligence & Data) through the Vinkius and every tool is automatically validated against Pydantic schemas — catch errors at build time, not in production.
ASK AI ABOUT THIS MCP SERVER
Vinkius supports streamable HTTP and SSE.
import asyncio
from pydantic_ai import Agent
from pydantic_ai.mcp import MCPServerHTTP
async def main():
# Your Vinkius token — get it at cloud.vinkius.com
server = MCPServerHTTP(url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")
agent = Agent(
model="openai:gpt-4o",
mcp_servers=[server],
system_prompt=(
"You are an assistant with access to Looker (Business Intelligence & Data) "
"(7 tools)."
),
)
result = await agent.run(
"What tools are available in Looker (Business Intelligence & Data)?"
)
print(result.data)
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.
Pydantic AI validates every Looker (Business Intelligence & Data) tool response against typed schemas, catching data inconsistencies at build time. Connect 7 tools through the Vinkius and switch between OpenAI, Anthropic, or Gemini without changing your integration code — full type safety, structured output guarantees, and dependency injection for testable agents.
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 Pydantic AI 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 Pydantic AI via MCP
Follow these steps to integrate the Looker (Business Intelligence & Data) MCP Server with Pydantic AI.
Install Pydantic AI
Run pip install pydantic-ai
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) with type-safe schemas
Why Use Pydantic AI with the Looker (Business Intelligence & Data) MCP Server
Pydantic AI provides unique advantages when paired with Looker (Business Intelligence & Data) through the Model Context Protocol.
Full type safety: every MCP tool response is validated against Pydantic models, catching data inconsistencies before they reach your application
Model-agnostic architecture — switch between OpenAI, Anthropic, or Gemini without changing your Looker (Business Intelligence & Data) integration code
Structured output guarantee: Pydantic AI ensures tool results conform to defined schemas, eliminating runtime type errors
Dependency injection system cleanly separates your Looker (Business Intelligence & Data) connection logic from agent behavior for testable, maintainable code
Looker (Business Intelligence & Data) + Pydantic AI Use Cases
Practical scenarios where Pydantic AI combined with the Looker (Business Intelligence & Data) MCP Server delivers measurable value.
Type-safe data pipelines: query Looker (Business Intelligence & Data) with guaranteed response schemas, feeding validated data into downstream processing
API orchestration: chain multiple Looker (Business Intelligence & Data) tool calls with Pydantic validation at each step to ensure data integrity end-to-end
Production monitoring: build validated alert agents that query Looker (Business Intelligence & Data) and output structured, schema-compliant notifications
Testing and QA: use Pydantic AI's dependency injection to mock Looker (Business Intelligence & Data) responses and write comprehensive agent tests
Looker (Business Intelligence & Data) MCP Tools for Pydantic AI (7)
These 7 tools become available when you connect Looker (Business Intelligence & Data) to Pydantic AI 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 Pydantic AI
Ready-to-use prompts you can give your Pydantic AI 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 Pydantic AI
Common issues when connecting Looker (Business Intelligence & Data) to Pydantic AI through the Vinkius, and how to resolve them.
MCPServerHTTP not found
pip install --upgrade pydantic-aiLooker (Business Intelligence & Data) + Pydantic AI FAQ
Common questions about integrating Looker (Business Intelligence & Data) MCP Server with Pydantic AI.
How does Pydantic AI discover MCP tools?
MCPServerHTTP instance with the server URL. Pydantic AI connects, discovers all tools, and generates typed Python interfaces automatically.Does Pydantic AI validate MCP tool responses?
Can I switch LLM providers without changing MCP code?
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 Pydantic AI
Get your token, paste the configuration, and start using 7 tools in under 2 minutes. No API key management needed.