How to Use the Coalesce MCP in Vercel AI SDK

Q: How do I connect Coalesce to my Vercel AI SDK app?

Install @ai-sdk/mcp and instantiate the client using the Vinkius MCP endpoint. Pass the tools array directly to streamText and remember to call mcpClient.close() once the generation finishes.

Q: How does Vercel AI SDK handle long-running Coalesce jobs?

The agent calls triggerjob to start the execution, then uses getrunstatus to poll the pipeline. Because the SDK supports streaming, your UI displays the incremental status updates to the user in real-time.

Watch your Vercel AI SDK app stream real-time Snowflake pipeline statuses and run updates directly from Coalesce.

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Connect Coalesce MCP to Vercel AI SDK

Create your Vinkius account to connect Coalesce 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 Coalesce with Vercel AI SDK

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Live pipeline monitoring in your Vercel AI SDK UI

The `list_environments` and `get_run_status` tools let your Vercel AI SDK application pull live pipeline structures and execution states directly into your React or Next.js frontend. Instead of making users wait for a full page reload, the agent streams the active environment setup and current run progress line-by-line as it fetches them. You build a clean, interactive dashboard where your users watch pipeline runs finish. This MCP integration feeds raw data straight into your streaming UI components, letting you render visual progress bars without building custom polling backends.

Instant job triggering without loading spinners

Using the `trigger_job` and `get_job_details` tools, your Vercel AI SDK client starts specific data warehouse tasks right from a chat prompt. Your Vercel AI SDK setup executes these operations at the edge, feeding the initial job ID and parameters straight into the chat stream. Users get immediate feedback that their Snowflake transformation started. The agent keeps the connection open, calling the API and updating the interface as the job steps execute.

Interactive node inspection on the edge

By calling the `list_nodes` tool, your Vercel AI SDK agent diagnoses pipeline bottlenecks using this MCP Server. It fetches the exact structural details of your Coalesce graph and outputs them as a clean, streamable JSON payload. This means developers inspect node schemas and configurations inside their chat interfaces. You don't write heavy boilerplate code because the SDK maps these tool outputs directly to your custom UI components.

Setup guide

Set up Coalesce 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 Coalesce 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 Coalesce transactions",
});

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

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 Coalesce. 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.

Why Choose Vinkius

Vinkius connects your tools to AI with real-time monitoring and automatic cost savings — all from one dashboard.

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Real-time monitoring

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Connect your favorite tools to your AI and see exactly what's happening — every request, every response, in real time.

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Common questions about Coalesce MCP in Vercel AI SDK

Install `@ai-sdk/mcp` and instantiate the client using the Vinkius MCP endpoint. Pass the tools array directly to `streamText` and remember to call `mcpClient.close()` once the generation finishes.

Yes, this MCP Server works perfectly in edge environments because it uses lightweight HTTP transport. Your edge route calls `trigger_run` or `trigger_job` and streams the response back to the client without running into execution timeout limits.

The agent calls `trigger_job` to start the execution, then uses `get_run_status` to poll the pipeline. Because the SDK supports streaming, your UI displays the incremental status updates to the user in real-time.

No, you do not. Vinkius manages the authentication securely on the backend, meaning your frontend only talks to the secure Vercel AI SDK endpoint.

Your Coalesce credentials and job metadata never touch Vercel's logging layers. Vinkius routes all requests through an ephemeral, zero-trust sandbox that discards execution data the moment your API call completes.

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