How to Use the HrFlow.ai MCP in Vercel AI SDK

Q: Can I use HrFlow.ai with Vercel AI SDK to filter candidates?

Yes, you can use the searchprofiles tool to run semantic queries. The results stream back to your agent, letting you filter through your database based on specific job requirements.

Stream HrFlow.ai candidate data directly into your Vercel AI SDK frontends for live talent insights.

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Connect HrFlow.ai MCP to Vercel AI SDK

Create your Vinkius account to connect HrFlow.ai 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 HrFlow.ai with Vercel AI SDK

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Real-time resume parsing in Vercel AI SDK

Stop forcing users to wait for backend processing. When you pass `parse_profile` to your streamText call, the structured candidate data hits your React components as soon as it's ready. Your UI updates instantly without those annoying loading spinners. It's the difference between a sluggish form and a fluid, modern recruiting dashboard.

Dynamic profile scoring for your interface

Feed your candidate matches directly into your AI client's render loop using `score_profiles`. You get the ranked data immediately, allowing your frontend to show match percentages as the agent thinks. This keeps your application state in sync with the actual hiring logic. You aren't just showing static results; you're showing the agent's reasoning process live.

Interactive candidate career pathing

Use `unfold_profile` to render complex career histories inside your Vercel AI SDK components. The tool breaks down years of experience into clean JSON objects your frontend can map to timelines. Developers can visualize these paths for recruiters immediately. It turns raw, messy resumes into clean, interactive UI elements.

Setup guide

Set up HrFlow.ai 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 HrFlow.ai 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 HrFlow.ai 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 HrFlow.ai. 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 HrFlow.ai MCP in Vercel AI SDK

Install the MCP packages and define your client using the HTTP transport. Once initialized, map the tools into your generateText function to start querying your talent data.

Yes, you can use the `search_profiles` tool to run semantic queries. The results stream back to your agent, letting you filter through your database based on specific job requirements.

It does. Since the connection relies on standard HTTP transport, you can keep your talent pipeline running efficiently at the edge.

The SDK treats these as standard tool execution failures. You should implement basic try-catch blocks around your tool calls to handle any API-side issues.

All resume files are encrypted in transit and at rest. Your agent only sees the parsed data returned by the server, ensuring raw sensitive documents stay secured within the HrFlow infrastructure.

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