How to Use the LeadConnector MCP in Vercel AI SDK

Q: How do I configure LeadConnector with Vercel AI SDK?

Install the @ai-sdk/mcp package and initialize the MCP client using the HTTP transport. Pass the tool definitions from mcpClient.tools() directly into generateText or streamText to let your agent query CRM data. Remember to call mcpClient.close() when the session ends to prevent memory leaks.

Q: Can I stream LeadConnector opportunity pipelines using Vercel AI SDK?

Yes. When your agent calls the listopportunities tool, the data streams chunk-by-chunk directly to your frontend components. This eliminates the delay of waiting for the full payload to resolve before rendering the UI.

Get live LeadConnector updates streaming straight into your Next.js UI using the Vercel AI SDK.

See Vinkius in Action

Works with every AI agent you already use

…and any MCP-compatible client

MCP Servers - Free for Subscribers

Connect LeadConnector MCP to Vercel AI SDK

Create your Vinkius account to connect LeadConnector 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.

GDPR Free for Subscribers

Setup LeadConnector with Vercel AI SDK

Ask AI about this MCP

ChatGPT

Claude

Perplexity

Build real-time sales dashboards with Vercel AI SDK

Stop making users stare at loading spinners while waiting for HighLevel updates. This integration lets your Vercel AI SDK interface stream raw pipeline data directly from the server. By calling `list_opportunities`, your Next.js application displays active deal stages the millisecond the LLM processes them. Your frontend handles the rendering while the edge-compatible Vercel AI SDK client manages the transport. It keeps the connection light and fast, which is exactly what you need for snappy, high-volume LeadConnector sales tools.

Sync live schedules without page refreshes

HighLevel calendar management usually means heavy API calls and constant UI reloads. This MCP Server changes that pattern by piping real-time updates directly into your Vercel AI SDK React components. Your agent calls `list_appointments` to pull the latest calendar slots and updates the view instantly. It uses the streamable HTTP transport under the hood of your Next.js app. You get a clean, reactive interface where users see their LeadConnector sales calls populate on the screen in real-time.

Zero-latency contact lookups at the edge

Waiting for backend lookups ruins the user experience when searching LeadConnector directories. With this setup, your Vercel AI SDK app calls `list_contacts` on edge functions to grab customer records instantly. The Vercel AI SDK handles the stream, letting the MCP client parse and display phone numbers and email addresses without hitting central database bottlenecks. It is fast, lightweight, and runs close to your LeadConnector sales reps.

Setup guide

Set up LeadConnector 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 LeadConnector 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 LeadConnector 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 LeadConnector (GoHighLevel). 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.

Connect LeadConnector now

Real-time monitoring

Live

visibility into every interaction

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

Built-in savings

60%

lower AI costs

Vinkius compresses data between your apps and your AI automatically. Lower bills every month — no configuration required.

Single dashboard

One

place for every integration

Every tool your AI connects to, managed from a single screen. One account, complete control.

Common questions about LeadConnector MCP in Vercel AI SDK

Install the `@ai-sdk/mcp` package and initialize the MCP client using the HTTP transport. Pass the tool definitions from `mcpClient.tools()` directly into `generateText` or `streamText` to let your agent query CRM data. Remember to call `mcpClient.close()` when the session ends to prevent memory leaks.

Yes. When your agent calls the `list_opportunities` tool, the data streams chunk-by-chunk directly to your frontend components. This eliminates the delay of waiting for the full payload to resolve before rendering the UI.

The server manages API throttling directly at the transport layer. If your agent makes rapid requests to `list_contacts`, the server queues them to stay within HighLevel's limits, keeping your edge functions from failing.

Yes. You can pass dynamic credentials through the SDK's provider options. This allows your application to query different HighLevel sub-accounts based on the active user session.

Your contact records, appointments, and pipeline opportunities never sit on Vercel's servers. The MCP Server acts as an ephemeral proxy, passing TLS-encrypted payloads directly to your local runtime without persistent caching.

Use it with your favorite AI tools

Connect this server to Cursor, Claude, VS Code, and more.

OpenAI Agents SDK sdk-python

Google ADK sdk-python

Pydantic AI sdk-python

Vercel AI SDK sdk-typescript