How to Use the Habitify MCP in Vercel AI SDK

Q: How do I display a user's daily habits for today?

Your agent can call getjournal for the current date to fetch all scheduled habits. This returns a clean list of what needs to be done, which you can map directly to a checklist in your UI.

Build live React dashboards with Vercel AI SDK that stream habit logs and progress updates directly from Habitify.

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Connect Habitify MCP to Vercel AI SDK

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

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Stream Live Habitify Logs via Vercel AI SDK

The `add_habit_log` tool records progress for a specific habit instantly within your streaming interface. When your users check off their morning run, they expect to see their progress immediately. Your application calls `mcpClient.tools()` to grab the necessary tools and hooks them into `streamText`. This lets your React or Next.js frontend render updated streaks instantly, keeping your users engaged with their daily routines.

Build Dynamic Habit Analytics

The `get_habit_stats` tool pulls granular performance metrics for any habit over a specified date range. Pulling habit historical data can slow down a user interface if you fetch everything at once. This MCP Server lets your agent pull granular metrics and stream the visual charts directly to the user's screen. You can write custom Edge Functions that call `list_habit_logs` to feed a lightweight visualization component.

Interactive Habit Creation Interfaces

The `create_habit` tool allows your agent to establish new recurring routines directly from a conversational prompt. Instead of building complex forms with endless input fields, let your users talk their habits into existence. If a user changes their mind, your agent uses the MCP connection to call `update_habit` to tweak the parameters on the fly. The entire interaction happens inside a single chat thread, making habit management feel like a conversation rather than data entry.

Setup guide

Set up Habitify 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 Habitify 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 Habitify 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 Habitify. 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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Common questions about Habitify MCP in Vercel AI SDK

You use the authProvider option when setting up your HTTP transport. This lets you securely pass the user's personal token to the MCP Server, ensuring they only view and modify their own personal habits.

Yes, you can. By passing the tools retrieved from mcpClient.tools() to streamText, the agent can invoke get_habit_stats and stream the resulting JSON data directly into your React components as it resolves.

Always call mcpClient.close() once your streaming session finishes. This prevents your Edge Functions from leaking open connections and keeps your serverless execution times low.

Your agent can call get_journal for the current date to fetch all scheduled habits. This returns a clean list of what needs to be done, which you can map directly to a checklist in your UI.

Absolutely. Your habit logs, completion stats, and journal entries are processed inside an isolated V8 sandbox. Vinkius handles the underlying credentials securely, meaning your raw API keys are never exposed to the client-side code.

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