How to Use the BLS Wages — OEWS Occupational Employment MCP in Vercel AI SDK

Q: How do I configure BLS Wages — OEWS Occupational Employment with Vercel AI SDK?

Install the @ai-sdk/mcp package and initialize the client using createMCPClient with your Vinkius HTTP endpoint. Pass the tools directly into streamText or generateText so your agent can run querybls dynamically.

Q: What is the best way to handle 50 concurrent wage queries in BLS Wages — OEWS Occupational Employment with Vercel AI SDK?

Group your series IDs into a single call or run parallel streamText operations on Vercel Edge Functions. The querybls tool supports up to 50 concurrent lookbacks, making it perfect for rapid parallel rendering.

Q: Do I need to manually parse the BLS Series IDs?

No. While the querybls tool requires explicit series IDs, you can instruct your agent to map common job titles and states to their respective numeric codes before making the call.

Pipe live US wage data directly into your Next.js frontend with Vercel AI SDK.

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Connect BLS Wages — OEWS Occupational Employment MCP to Vercel AI SDK

Create your Vinkius account to connect BLS Wages — OEWS Occupational Employment 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 BLS Wages — OEWS Occupational Employment with Vercel AI SDK

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Stream real-time wage data using Vercel AI SDK

The `query_bls` tool lets you stream live wage statistics directly from the Bureau of Labor Statistics into your React components using Vercel AI SDK. Stop making your users stare at stale loading spinners when they search for salary trends. By hooking this MCP server up to Vercel AI SDK, your application streams live wage statistics directly from the Bureau of Labor Statistics into your React components. The moment your agent fires the query, raw numbers paint on the screen in real-time. Your AI client handles the complex series codes behind the scenes, leaving your frontend responsive. You get a clean, reactive interface where users watch salary charts build themselves line by line.

Fire 50 concurrent wage queries on the edge

Running up to 50 concurrent lookbacks with `query_bls` lets your Vercel AI SDK edge functions query massive wage datasets simultaneously. This MCP setup lets your agent trigger multiple parallel requests to compare regional earnings across different states. Build rapid-fire comparison tools that fetch median pay for dozens of jobs at once. The edge runtime keeps latency low while your agent organizes the massive payload.

Clean lifecycle management for BLS series lookups

Integrating `query_bls` into your codebase is simple when you pass the tools directly to Vercel's `streamText` function. Clean lifecycle management starts with `createMCPClient` and ends with a quick call to `mcpClient.close()` to prevent resource leaks. Letting your agent decide when to pull occupational statistics keeps your serverless execution times tight. You avoid hanging connections and keep your API routes incredibly fast.

Setup guide

Set up BLS Wages — OEWS Occupational Employment 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 BLS Wages — OEWS Occupational Employment 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 BLS Wages — OEWS Occupational Employment transactions",
});

console.log(text);
await mcpClient.close();

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Common questions about BLS Wages — OEWS Occupational Employment MCP in Vercel AI SDK

Install the `@ai-sdk/mcp` package and initialize the client using `createMCPClient` with your Vinkius HTTP endpoint. Pass the tools directly into `streamText` or `generateText` so your agent can run `query_bls` dynamically.

Yes. The Vercel AI SDK streams the tool calls and execution results directly to your frontend UI. Your users see the wage data render instantly as the `query_bls` tool returns the payload.

Group your series IDs into a single call or run parallel `streamText` operations on Vercel Edge Functions. The `query_bls` tool supports up to 50 concurrent lookbacks, making it perfect for rapid parallel rendering.

No. While the `query_bls` tool requires explicit series IDs, you can instruct your agent to map common job titles and states to their respective numeric codes before making the call.

Vinkius runs the server in an isolated, zero-trust sandbox. Your specific wage queries and numeric series codes are processed in an ephemeral container that is destroyed immediately after execution, ensuring your HR research remains completely private.

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