How to Use the Lead Time Analyzer MCP in Vercel AI SDK

Stream supply chain data directly into your Vercel AI SDK interface to visualize bottlenecks as they happen.

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Works with every AI agent you already use

…and any MCP-compatible client

MCP Servers — Included with Plan

Connect Lead Time Analyzer MCP to Vercel AI SDK

Create your Vinkius account to connect Lead Time Analyzer to Vercel AI SDK — we handle the hosting, security, and runtime updates so you don't have to. No server setup required.

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Setup Lead Time Analyzer with Vercel AI SDK

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Real-time lead time decomposition

Pipe the output of `analyze_lead_time_composition` straight into your frontend components. Users see the exact breakdown of their supply chain segments without waiting for a backend refresh. This MCP pushes data through your edge functions to keep the UI snappy. It turns raw logistics numbers into a live dashboard for your end users.

Simulated impact forecasting

Feed the results of `calculate_reduction_impacts` into your app to show users how specific changes alter their delivery dates. It generates immediate feedback for supply chain adjustments. Your interface handles the stream so the user watches the numbers adjust in real time. It removes the guesswork from capacity planning.

Volatility monitoring for your AI client

Use `evaluate_process_volatility` to flag unstable stages in your delivery pipeline. It identifies the segments causing the most unpredictable shipping delays. Integrating this MCP gives your app the ability to alert users when a specific leg of the journey becomes erratic. The data doesn't lie when it comes to process instability.

Setup guide

Set up Lead Time Analyzer 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 Lead Time Analyzer 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 Lead Time Analyzer 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 Lead Time Analyzer. 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

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Built-in savings

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Common questions about Lead Time Analyzer MCP in Vercel AI SDK

You install the client library and initialize the connection via HTTP transport. Once you pass the tool definitions to your streamText function, the data flows directly into your logic.

Yes, the streaming nature of the SDK allows tool outputs to update your UI instantly. You avoid the lag associated with traditional request-response cycles.

It includes support for OAuth through the standard authProvider configuration. You can manage access securely while keeping your supply chain analysis private.

This MCP is designed to run in environments where speed matters. It handles the request-response handshake efficiently to stay within your edge execution limits.

The MCP only processes your provided logistics timestamps and transit logs. It does not store your proprietary shipment records on our servers.

Use it with your favorite AI tools

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