How to Use the Azure Synapse Analytics MCP in Vercel AI SDK

Q: How do I connect Azure Synapse Analytics to the Vercel AI SDK?

Run npm install ai @ai-sdk/mcp in your project. Then initialize the connection using createMCPClient with your HTTP transport URL. Pass the resulting tools directly to streamText.

Q: Can the Vercel AI SDK stream Azure Synapse Analytics notebook lists?

Yes, the listnotebooks tool feeds directly into the streaming response. Your React components will render the Spark notebook names as the model processes them.

Q: Does the Vercel AI SDK support OAuth for Azure Synapse Analytics?

You can pass an authProvider during client initialization. This handles the authentication flow before the application requests pipeline or dataset information.

Stream live Azure Synapse Analytics infrastructure data directly into your React frontends using the Vercel AI SDK.

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Connect Azure Synapse Analytics MCP to Vercel AI SDK

Create your Vinkius account to connect Azure Synapse 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 Azure Synapse Analytics with Vercel AI SDK

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Stream Synapse pipelines via Vercel AI SDK

The `get_pipeline` and `list_pipelines` tools let your frontend render data integration jobs in real-time. When a user asks for pipeline definitions, the Vercel AI SDK streams the JSON schema straight into your React components without making them wait for a loading spinner. You configure the MCP client using `createMCPClient` with an HTTP transport. Once connected, your application pulls the exact configuration of data movement tasks and renders them incrementally.

Expose SQL and Spark pool status

Calling `list_spark_pools` and `list_sql_pools` feeds live compute infrastructure states right into your user interface. Your end users see dedicated and serverless SQL pool availability instantly as the language model streams the response back. Passing these tools into `streamText` means you build dashboards where administrators can check pre-provisioned Apache Spark environments. The connection stays fast and responsive, avoiding the usual latency of traditional API polling.

Render datasets and linked services

Using `list_datasets` and `list_linked_services` gives your application immediate access to explicit target definitions. The language model processes these external connections and streams the dataset parameters directly to your Svelte or Vue frontend. Developers just pass the `mcpClient.tools()` array into the generation call. Finally, remember to fire `mcpClient.close()` when the session ends to cleanly sever the HTTP connection.

Setup guide

Set up Azure Synapse 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 Azure Synapse 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 Azure Synapse Analytics transactions",
});

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

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Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Azure Synapse Analytics. 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 Azure Synapse Analytics MCP in Vercel AI SDK

Run `npm install ai @ai-sdk/mcp` in your project. Then initialize the connection using `createMCPClient` with your HTTP transport URL. Pass the resulting tools directly to `streamText`.

Yes, the `list_notebooks` tool feeds directly into the streaming response. Your React components will render the Spark notebook names as the model processes them.

You need to configure an HTTP transport when setting up the client. Vinkius provides the endpoint URL you pass into the transport configuration object.

You can pass an `authProvider` during client initialization. This handles the authentication flow before the application requests pipeline or dataset information.

Vinkius runs the server in an ephemeral V8 Isolate Sandbox. The process reads your data integration structures and linked service configurations, returns the results to your frontend, and immediately destroys the environment. Zero data persists on our infrastructure.

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