How to Use the User-Agent Parser MCP in Vercel AI SDK

Stream structured client data into your UI using the Vercel AI SDK.

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Connect User-Agent Parser MCP to Vercel AI SDK

Create your Vinkius account to connect User-Agent Parser 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 User-Agent Parser with Vercel AI SDK

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Accurate Client Detection for Your Agent

The `parse_ua` tool decodes raw HTTP User-Agent strings instantly. You get structured JSON containing Browser, OS, and Device specs. This stops your AI client from guessing client details when it reads a log file. It gives you the actual environment data needed for accurate responses.

Real-time Log Analysis with MCP Server

Use `parse_ua` to analyze server logs and pipe the findings directly into your streaming UI. You pass the raw UA string, and the tool returns clean JSON. Instead of showing a loading spinner while data is processed, the user sees the structured results appear live in React or Next.js.

Conditional Response Generation

You can check the client's environment before generating text. For example, if `parse_ua` detects a mobile OS, your agent knows to format the output differently. This lets you build user-facing products that adapt their response structure based on the detected device.

Setup guide

Set up User-Agent Parser 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 User-Agent Parser 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 User-Agent Parser transactions",
});

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

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Common questions about User-Agent Parser MCP in Vercel AI SDK

It feeds structured client specs directly to your streamText function. You pass a messy log string, and instead of getting garbage, you get clean JSON identifying Browser, OS, and Device.

The tool extracts three core pieces of information: the specific Browser, the Operating System, and the Device type. It turns raw strings into easily usable JSON objects.

Absolutely. Since it's an MCP Server, its structured output streams directly into your client's UI context. This keeps the experience fast and responsive.

This server processes raw HTTP User-Agent strings and outputs structured client specifications (Browser, OS, Device). You must treat the original UA string as potentially sensitive log data.

Yes. It solves the problem of hallucination by forcing a structured read. When you pass historical log lines, `parse_ua` gives you reliable client specs.

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