How to Use the Metabase (Business Intelligence & Analytics) MCP in Vercel AI SDK

Q: Can Vercel AI SDK agents pull raw SQL from Metabase (Business Intelligence & Analytics)?

Yes, the agent uses getcard to retrieve the configuration of any visual question. This gives your agent access to the underlying query logic for further analysis.

Q: How are dashboard layouts retrieved for my UI?

You use getdashboard to fetch the specific layout matrix. Your front-end then parses this structure to render the grid of cards exactly as they appear in the BI platform.

Stream real-time Metabase (Business Intelligence & Analytics) insights directly into your Vercel AI SDK frontends.

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Connect Metabase (Business Intelligence & Analytics) MCP to Vercel AI SDK

Create your Vinkius account to connect Metabase (Business Intelligence & Analytics) to Vercel AI SDK 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 Metabase (Business Intelligence & Analytics) with Vercel AI SDK

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Real-time BI data streaming

Your React or Next.js components can now render live reporting data directly from your warehouse. By using `get_card` and `get_dashboard`, your UI fetches specific metrics without waiting for slow server-side reloads. This setup connects your front-end state to your actual data layer. Your users get immediate visibility into key metrics as the AI processes the underlying query results.

Instant dashboard discovery

Stop hardcoding report IDs in your source code. Use `list_dashboards` to dynamically populate your navigation menus or sidebar filters based on the current state of your BI instance. This allows your application to adapt to changes in your analytics structure automatically. When a new report appears in the platform, your users see it instantly without a deployment.

Deep content searching

Surface buried reports by letting your agent navigate your entire data library. The `search_content` tool allows your AI client to find specific cards or collections across your entire organization. It removes the need for users to remember exact naming conventions. Your agent finds the right data entity and pulls the relevant card details into the view.

Setup guide

Set up Metabase (Business Intelligence & Analytics) MCP in Vercel AI SDK

Prerequisites

Node.js 18+ and a TypeScript project
ai + @modelcontextprotocol/sdk packages
Active Vinkius subscription with a valid endpoint token

1

Install dependencies

Run npm install ai @modelcontextprotocol/sdk plus your preferred model provider (e.g. @ai-sdk/openai).
2

Create the Streamable HTTP transport

Use StreamableHTTPClientTransport with your Vinkius endpoint URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.
3

Discover and use tools

Call mcpClient.tools() to auto-discover all Metabase (Business Intelligence & Analytics) tools. Pass them directly to generateText() or streamText() — no manual schema definitions needed.
4

Works with any model provider

Swap openai("gpt-4o") for any AI SDK provider — Anthropic, Google, Mistral. The MCP tools work identically across all supported models.

index.ts

import { experimental_createMCPClient as createMCPClient } from "ai";
import { StreamableHTTPClientTransport } from "@modelcontextprotocol/sdk/client/streamableHttp";
import { generateText } from "ai";
import { openai } from "@ai-sdk/openai";

const transport = new StreamableHTTPClientTransport(
  new URL("https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")
);

const mcpClient = await createMCPClient({ transport });
const tools = await mcpClient.tools();

const { text } = await generateText({
  model: openai("gpt-4o"),
  tools,
  prompt: "List recent Metabase (Business Intelligence & Analytics) transactions",
});

console.log(text);
await mcpClient.close();

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Common questions about Metabase (Business Intelligence & Analytics) MCP in Vercel AI SDK

You integrate the search tool into your agent's capability list. Once configured, your Vercel AI SDK agent executes `search_content` to locate specific cards or dashboards by keyword.

Yes, the agent uses `get_card` to retrieve the configuration of any visual question. This gives your agent access to the underlying query logic for further analysis.

The server functions as an MCP endpoint that your client connects to over HTTP. This setup allows your edge functions to request data from your BI instance as needed.

You use `get_dashboard` to fetch the specific layout matrix. Your front-end then parses this structure to render the grid of cards exactly as they appear in the BI platform.

The server only reads metadata and query results from your instance. It handles communication via an endpoint token, ensuring your BI data remains isolated from external access.

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