Does this work with self-hosted Metabase instances?

Yes. You just need to provide your full Metabase Base URL (e.g., http://metabase.your-domain.com). Your agent will connect securely to your private instance using your API Key.

Can I see the SQL code behind a specific Metabase card through my agent?

Yes. Use the `get_card` tool with a specific Card ID. Your agent will retrieve the complete mapping logic, exposing the underlying SQL query (if it's a SQL question) or the structural query parameters used to generate the visualization.

Can my agent list all databases currently connected to Metabase?

Absolutely. Use the `list_databases` tool to identify all physical data warehouse integrations. Your agent will report the database names, types (e.g., PostgreSQL, Snowflake), and internal IDs available for querying.

Metabase (Business Intelligence & Analytics) MCP Server for LangChain 7 tools — connect in under 2 minutes

Name: Metabase (Business Intelligence & Analytics)
Availability: InStock
Author: Vinkius

python.langchain.com/docs/integrations/tools/mcp

Built by Vinkius GDPR 7 Tools Framework

LangChain is the leading Python framework for composable LLM applications. Connect Metabase (Business Intelligence & Analytics) through the Vinkius and LangChain agents can call every tool natively — combine them with retrievers, memory, and output parsers for sophisticated AI pipelines.

Get MCP Server for AI Agents

ASK AI ABOUT THIS MCP SERVER

Open in ChatGPT Open in Claude Open in Perplexity

Vinkius supports streamable HTTP and SSE.

python

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({
        "metabase-business-intelligence-analytics": {
            "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 Metabase (Business Intelligence & Analytics), show me what tools are available.",
            }]
        })
        print(response["messages"][-1].content)

asyncio.run(main())

Metabase (Business Intelligence & Analytics)

Fully ManagedVinkius Servers

60%Token savings

High SecurityEnterprise-grade

IAMAccess control

EU AI ActCompliant

DLPData protection

V8 IsolateSandboxed

Ed25519Audit chain

<40msKill switch

Stream every event to Splunk, Datadog, or your own webhook in real-time

* 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 Metabase (Business Intelligence & Analytics) MCP Server

Connect your Metabase instance to any AI agent and take full control of your enterprise business intelligence, data visualizations, and reporting through natural conversation.

LangChain's ecosystem of 500+ components combines seamlessly with Metabase (Business Intelligence & Analytics) 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 reporting panels and retrieve detailed layout matrices, including specific card mappings and filter structures directly from your agent
Question & Card Audit — Access visual 'Cards' to retrieve the underlying SQL logic or visualization definitions driving your key business metrics securely
Global Content Search — Execute cross-platform searches to identify Dashboards, Cards, or Data tables matching specific keywords across your entire Metabase instance
Collection Navigation — Explore hierarchical BI Collections (folders) to understand your reporting taxonomy and organizational data boundaries natively
Database Inventory — Enumerate physical data warehouse connections and raw table mappings available for querying within your Metabase environment
Metadata Inspection — Deep-dive into specific Dashboard or Card IDs to retrieve precise JSON representations and chronological data insights instantly

The Metabase (Business Intelligence & Analytics) 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 Metabase (Business Intelligence & Analytics) to LangChain via MCP

Follow these steps to integrate the Metabase (Business Intelligence & Analytics) 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 Metabase (Business Intelligence & Analytics) via MCP

Why Use LangChain with the Metabase (Business Intelligence & Analytics) MCP Server

LangChain provides unique advantages when paired with Metabase (Business Intelligence & Analytics) through the Model Context Protocol.

The largest ecosystem of integrations, chains, and agents — combine Metabase (Business Intelligence & Analytics) 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 Metabase (Business Intelligence & Analytics) queries for multi-turn workflows

Metabase (Business Intelligence & Analytics) + LangChain Use Cases

Practical scenarios where LangChain combined with the Metabase (Business Intelligence & Analytics) MCP Server delivers measurable value.

RAG with live data: combine Metabase (Business Intelligence & Analytics) tool results with vector store retrievals for answers grounded in both real-time and historical data

Autonomous research agents: LangChain agents query Metabase (Business Intelligence & Analytics), synthesize findings, and generate comprehensive research reports

Multi-tool orchestration: chain Metabase (Business Intelligence & Analytics) tools with web scrapers, databases, and calculators in a single agent run

Production monitoring: use LangSmith to trace every Metabase (Business Intelligence & Analytics) tool call, measure latency, and optimize your agent's performance

Metabase (Business Intelligence & Analytics) MCP Tools for LangChain (7)

These 7 tools become available when you connect Metabase (Business Intelligence & Analytics) to LangChain via MCP:

get_card

Fetch explicit mapping logic behind a specific BI Card

get_dashboard

Get comprehensive Metabase Dashboard layout matrices

list_cards

Returns heavy lists. List raw questions (cards) parsed inside Metabase

list_collections

List structural BI Collections (folders)

list_dashboards

List dashboards mapped in the Metabase BI platform

list_databases

List physical Database integrations parsed by Metabase

search_content

Search global reporting entities across Metabase

Example Prompts for Metabase (Business Intelligence & Analytics) in LangChain

Ready-to-use prompts you can give your LangChain agent to start working with Metabase (Business Intelligence & Analytics) immediately.

"List the last 5 dashboards created in my Metabase instance"

"Search for any Metabase content related to 'Revenue'"

"Show me the configuration for card ID '654'"

Troubleshooting Metabase (Business Intelligence & Analytics) MCP Server with LangChain

Common issues when connecting Metabase (Business Intelligence & Analytics) to LangChain through the Vinkius, and how to resolve them.

MultiServerMCPClient not found

Install: pip install langchain-mcp-adapters

Metabase (Business Intelligence & Analytics) + LangChain FAQ

Common questions about integrating Metabase (Business Intelligence & Analytics) MCP Server with LangChain.

How does LangChain connect to MCP servers?

Use 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?

All agent types including ReAct, OpenAI Functions, and custom agents work with MCP tools. The tools appear as standard LangChain tools after the adapter wraps them.