How to Use the Onpipeline MCP in Vercel AI SDK

Q: Can the Vercel AI SDK create new Onpipeline organizations?

Yes. The agent calls createcrmorganization based on user prompts. It maps the company name and details from the chat directly into the CRM database.

Stream live Onpipeline CRM deals and contacts directly into your Next.js frontend using the Vercel AI SDK.

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Connect Onpipeline MCP to Vercel AI SDK

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

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Stream Live Deals to Next.js

`list_crm_deals` and `list_pipelines` fetch current sales data for your Vercel AI SDK application. Your users see deals appear instantly in the UI without waiting for a loading spinner. The agent pulls the exact pipeline stages and deal values needed for the view. Streaming this data means the frontend updates as the model processes the request. You build a dashboard where account executives query their forecast, and the results render row by row.

Build Interactive Deal Creation UIs

`create_crm_contact` and `create_crm_deal` insert new entries into Onpipeline straight from a chat interface. The client captures user input, maps it to the required fields, and executes the creation. Forecasted revenue attaches to the deal immediately. End users don't have to leave your application to update the CRM. They type a quick note about a new lead, and the agent handles the API calls to set up the organization and the corresponding deal.

Onpipeline MCP Server Activity Tracking

`list_activities` and `list_crm_events` pull calendar events and logged tasks into your React or Svelte views. The agent reads the upcoming schedule for a specific rep. It filters out the noise and returns only the action items. Reps get a clear look at their daily requirements directly in your custom interface. You control the presentation while the server handles the underlying data fetching from Onpipeline.

Setup guide

Set up Onpipeline 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 Onpipeline 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 Onpipeline 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 Onpipeline. 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 Onpipeline MCP in Vercel AI SDK

Use createMCPClient with an HTTP transport URL. Pass the resulting mcpClient.tools() into your streamText call. Always remember to call mcpClient.close() when the session ends.

Yes. The agent calls create_crm_organization based on user prompts. It maps the company name and details from the chat directly into the CRM database.

It does. You pass an authProvider in the configuration. This ensures the user only accesses pipelines they have permission to see.

The tool returns an error block to the agent. You handle this in your application logic, usually by prompting the user for missing fields before retrying.

The server processes email addresses, phone numbers, and names strictly in memory. Vinkius runs the connection inside an ephemeral V8 Isolate Sandbox. Data passes through the zero-trust environment and immediately disappears after the API returns the payload.

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