How to Use the ChartHop MCP in Vercel AI SDK

Q: Can I show a user's direct reports from ChartHop in my Vercel AI SDK app?

Yes. Your agent can use the getpersondetails tool. The response includes reporting structures, which you can then render in your React or Next.js components.

Q: What's the setup for connecting the Vercel AI SDK to this ChartHop server?

It's a few lines of code. You'll npm install ai @ai-sdk/mcp, then use createMCPClient with the Vinkius URL. Pass the resulting tools to your AI model call.

Q: Is this just for reading data?

This specific server is read-only. It provides tools like listorganizationpeople and getjobdetails for securely accessing your org data. You won't be making changes to ChartHop with these tools.

Stream ChartHop org data directly into your Vercel AI SDK app. No spinners, just live results.

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Connect ChartHop MCP to Vercel AI SDK

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

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Build Live Org Charts

Connect the Vercel AI SDK to ChartHop and your UI can populate itself in real time. Your agent can call `list_organization_people` and `get_person_details`, and the data streams directly into your React components. Users see names and titles appearing one by one, not all at once after a long wait. This isn't just for people. You can build interfaces that show job listings from `list_organization_jobs` or department structures from `list_organization_departments`. The MCP Server handles the connection, so you just focus on what your UI does with the incoming data stream.

Interactive Headcount Planning

Let managers explore headcount scenarios without leaving your app. The `list_planning_scenarios` tool gives your agent access to compensation and staffing plans inside ChartHop. With the AI SDK, you can build a UI where a manager asks a question and sees the potential org structures render live. Imagine a manager asking, "Show me the 'Q3 Growth' scenario for Engineering." Your Vercel AI SDK-powered agent fetches the data, and your frontend components update as the information arrives. It feels interactive and immediate, which is exactly what you want for planning tools.

Your ChartHop MCP Server

This isn't a generic API. It's an MCP Server that exposes specific ChartHop actions as tools for your agent. When your code calls `get_job_details`, it's not just hitting an endpoint; it's invoking a secure, sandboxed operation managed by Vinkius. You get a single endpoint and token. The Vercel AI SDK's `createMCPClient` makes the connection trivial. You don't manage auth or infrastructure, you just build the frontend experience that uses the ChartHop data.

Setup guide

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

The integration is built-in. After setting up the MCP client, use `streamText` and pass it the tools. The AI SDK automatically handles streaming tool results from the ChartHop server into your UI.

Yes. Your agent can use the `get_person_details` tool. The response includes reporting structures, which you can then render in your React or Next.js components.

It's a few lines of code. You'll `npm install ai @ai-sdk/mcp`, then use `createMCPClient` with the Vinkius URL. Pass the resulting tools to your AI model call.

This specific server is read-only. It provides tools like `list_organization_people` and `get_job_details` for securely accessing your org data. You won't be making changes to ChartHop with these tools.

Your connection is secured by a unique Vinkius token. The server only accesses ChartHop people, job, and department data when your agent explicitly calls a tool. All operations run in an ephemeral, zero-trust sandbox.

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