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

Q: How does Looker (Business Intelligence & Data) handle schema errors in AutoGen debates?

If a tool call like runinlinequery returns an API error, the agent receives the raw message. It can then discuss the error with other agents to correct the query parameters.

Let AutoGen agents debate BI metrics and execute Looker queries to reach analytical consensus.

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Connect Looker (Business Intelligence & Data) MCP to AutoGen

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

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Let AutoGen agents audit your Looker MCP Server assets

Set up a multi-agent workflow where one agent lists dashboards and another checks them for consistency. Using `list_dashboards` and `get_dashboard`, your agents can compare dashboard structures and flag anomalies. The conversation-driven approach means a data quality agent can challenge a reporting agent's setup before any metrics are presented to the user.

Debate and run inline queries

When a business question is ambiguous, your AutoGen agents can discuss which dimensions to query. Once they agree, this MCP Server executes `run_inline_query` to fetch the actual numbers. If the query fails or returns unexpected empty sets, the agents can refine the query parameters and try again without human intervention.

Automated content curation across folders

Let your agents clean up your Looker instance. An archivist agent can call `list_folders` and `search_content` to find old, unused Looks that haven't been opened in months. By retrieving look details via `get_look`, the agents can decide whether to keep, move, or flag the asset for deletion based on your company's retention policies.

Setup guide

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

Prerequisites

Python 3.10+ installed
autogen-ext[mcp] package
Active Vinkius subscription with a valid endpoint token

1

Install AutoGen with MCP
Run pip install "autogen-ext[mcp]" autogen-agentchat. The MCP extension includes mcp_server_tools for stateless tool access.
2

Fetch tools from the MCP
Call mcp_server_tools(SseServerParams(url=...)) with your Vinkius endpoint. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.
3

Run your agent
Pass the tools to AssistantAgent and call agent.run(). The agent invokes Looker (Business Intelligence & Data) tools and returns structured results.

agent.py

from autogen_ext.tools.mcp import SseServerParams, mcp_server_tools
from autogen_agentchat.agents import AssistantAgent
from autogen_ext.models.openai import OpenAIChatCompletionClient

server_params = SseServerParams(
    url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)

tools = await mcp_server_tools(server_params)

agent = AssistantAgent(
    name="Looker (Business Intelligence & Data)_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

result = await agent.run("List recent Looker (Business Intelligence & Data) data")
print(result.messages[-1].content)

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Prerequisites

Python 3.10+ installed
autogen-ext[mcp] + autogen-agentchat
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Same packages as above. McpWorkbench is ideal when your agent needs stateful sessions across multiple tool calls.
2

Use McpWorkbench as context manager
Wrap your agent in async with McpWorkbench(...) to maintain shared state and resources. The workbench manages the full MCP session lifecycle.
3

Run with workbench
Pass workbench=workbench to your agent. State is preserved across multiple tool calls within the same session.

agent.py

from autogen_ext.tools.mcp import McpWorkbench, SseServerParams
from autogen_agentchat.agents import AssistantAgent
from autogen_ext.models.openai import OpenAIChatCompletionClient

server_params = SseServerParams(
    url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)

async with McpWorkbench(server_params) as workbench:
    agent = AssistantAgent(
        name="Looker (Business Intelligence & Data)_assistant",
        model_client=OpenAIChatCompletionClient(model="gpt-4o"),
        workbench=workbench,
    )

    result = await agent.run("List recent Looker (Business Intelligence & Data) data")
    print(result.messages[-1].content)

Get your connection token →

Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Looker. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.

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

You register the Looker tools with your AutoGen assistant agent using the MCP tool adapter. The agent then calls `run_inline_query` during its turn in the conversation.

Yes, all agents in your group chat can share the same tool definitions. One agent can call `list_looks` while another uses `get_look` to inspect the results.

If a tool call like `run_inline_query` returns an API error, the agent receives the raw message. It can then discuss the error with other agents to correct the query parameters.

The integration supports both stdio and streamable HTTP transports. This lets you run the server locally or host it in a secure remote environment.

No, the data stays localized inside your execution loop. The MCP server processes your Looker credentials and query results securely within a zero-trust sandbox, ensuring no raw business metrics leak outside your environment.

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