Vercel AI SDK

Use Reading Time Estimator with Vercel AI SDK

Set up in seconds, once. From then on, Vercel AI SDK gets things done for you: add one client call, and Reading Time Estimator is live in your Vercel AI SDK app.

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Reading Time Estimator Connector

You're all set. Choose your MCP client and follow the setup instructions.

Connector linkhttps://edge.vinkius.com/vk_preview_Cg64YXjoIFalKhxrt7x6Kon0d5H1qc9NJafZwHo2/mcp

Connecting Reading Time Estimator to the Vercel AI SDK

About a minute, in your code

  1. 1Install the MCP client: npm install @ai-sdk/mcp, and import createMCPClient from '@ai-sdk/mcp'.
  2. 2Create the client with your Reading Time Estimator link as the transport: { type: 'http', url: '<your link>' }. The link above is the URL.
  3. 3Call client.tools() and pass the tools to streamText. Reading Time Estimator is now part of your AI SDK app.
Configuration · claude_desktop_config.jsonCopy
{
  "mcpServers": {
    "reading-time-estimator-mcp": {
      "url": "https://edge.vinkius.com/vk_preview_Cg64YXjoIFalKhxrt7x6Kon0d5H1qc9NJafZwHo2/mcp"
    }
  }
}

Vercel AI SDK + Reading Time Estimator

One chat. Your Reading Time Estimator, live inside Vercel AI SDK.

Real prompts, answered with live data. This is the kind of interaction your app will have once the client is in.

Waiting for input…

Works with modern AI clients that support MCP, including ChatGPT, Claude, Cursor, and more.

ChatGPTClaudeCursorPerplexityGeminiMicrosoft CopilotRaycastMeta AI

These prompts ship with the Reading Time Estimator Connector. Once yours is connected, they work in your sessions.

The full capability set, the prompts and the observed latency live on the Reading Time Estimator Connector page.

Mission Control

See everything your Vercel AI SDK agents do in Reading Time Estimator.

When your app runs a tool in Reading Time Estimator, it happens inside your code: you see the output, but not the request itself, what data it carried, or whether a rule was broken.

Now every move lands on your screen.

Every execution is recorded, enforced, and shown to you: what ran, what it returned, what was blocked, and why.

AI Briefing
Upgrade to unlock

Know exactly what needs your attention before you even look at the charts. One briefing. The full picture. The next move.

Requests
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Total in this period
Avg Latency
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API 88ms · Overhead 54ms
Vinkius Reliability
0
Agent 280 · Upstream 47 · Vinkius 15
Tokens
0
AI tokens processed
DLP Protected
0
Sensitive data redacted
Cost Saved
0
FinOps truncations applied

AI Agents Activities

last 30 days
30d agotoday

Request Volume & Latency

requests latency

Every request your Vercel AI SDK agents make through Reading Time Estimator is logged, enforced and auditable. See how AI Governance turns agent activity into accountability, and what it means for your Reading Time Estimator workflows.

What sets us apart

It's not just the Connector that stays safe. Everything your agents do through it is.

Connecting Reading Time Estimator to your AI is step one. But an open line between your agents and Reading Time Estimator is only half the story: without protection, every action they take runs unguarded.

That's why protection doesn't stop at the door.

Every action your agents take through the Connector runs inside our infrastructure: signed activity logs, automatic key rotation, spending limits that pause instead of surprise, and a sandbox that isolates every execution.

01

Tamper Proof Activity Log

Every request your agents make in Reading Time Estimator is recorded and signed, so any change to the history becomes detectable.

02

Automatic Key Rotation

We rotate security keys every 24 hours. Your Reading Time Estimator connection stays protected without any manual work.

03

Spending Protection

We enforce the spending limit you choose and pause actions that exceed it until approval is received. Your Reading Time Estimator actions never surprise you.

04

Isolated Sandbox

Every execution through Reading Time Estimator runs isolated, with 34+ security rules covering memory, CPU, network access, file handling and execution.

05

Security Event Streaming

Security events flow to Splunk, Datadog or your own webhook, so Reading Time Estimator activity lands in the tools you already rely on.

06

Instant Resume

Idle connections resume in 3 to 5 ms while preserving their state, keeping your Vercel AI SDK sessions responsive.

07

Malicious File Protection

We detect compressed files designed to exhaust system resources and block them before they ever reach Reading Time Estimator.

08

Credential Validation

We test your Reading Time Estimator credentials before saving them. Invalid credentials are rejected instead of being stored.

09

Separate Usage Limits

Usage stays isolated per account, so activity in one connection never consumes another account's allowance.

One Connector, one link, and the whole operation is protected. Not just the connection: everything your agents do through it.

These guarantees are not add-ons: they are the infrastructure every Reading Time Estimator connection runs on. Read how our infrastructure protects everything your Vercel AI SDK agents do, end to end.

Inside Vinkius

Get Reading Time Estimator ready for your AI. It's easy.

You choose Reading Time Estimator from the Vinkius Catalog and get one connection link. That's it.

  1. You choose Reading Time Estimator

    You find Reading Time Estimator in the Catalog and install the Connector with one click.

    Reading Time Estimator

    Active

    Enable Connector
    Activating…

  2. You add the link in your code

    Vinkius gives you one connection link. In your app, you create an MCP client with the link as the HTTP transport URL, and the tools are ready.

    Token generated successfully

    MCP Connection URL

    https://edge.vinkius.com/vk_preview_Cg64YXjoIFalKhxrt7x6Kon0d5H1qc9NJafZwHo2/mcp

    Connection Token

    vk_live_••••••••
    Go to Dashboard

Set it up once. Use Reading Time Estimator with the AI you already use.

FAQ

Vercel AI SDK access questions.

  • 01

    Which package does this use?

    @ai-sdk/mcp, with the createMCPClient function. HTTP and SSE transports are supported, with optional headers and OAuth via an authProvider.

  • 02

    If I connect Reading Time Estimator to my app, can I use the same connection in Claude or ChatGPT later?

    Yes, and that's the point: the connection is yours, not the app's. You paste the same link in any AI you use, and there's nothing new to set up or pay again. Connect a new AI and Reading Time Estimator is already there.

  • 03

    Do I need to hand my Reading Time Estimator password to my app?

    Never. You sign in once at Vinkius, and we keep your logins encrypted and away from your AI. Your code only ever sees the link, and you can remove it whenever you want.

  • 04

    What can my app actually do with Reading Time Estimator?

    Whatever the Connector covers: check data, run actions and bring answers back. Pass the tools to streamText and the model picks the right one. The Connector page lists every capability, with real examples you can copy.

  • 05

    The tools don't show up. What do I check?

    Confirm the url matches your link exactly and that the Connector is active in your Vinkius account, then call tools() again. A deactivated Connector stops responding.

  • 06

    Can I remove the access later?

    Anytime. Close the client and remove the tools from your call, and Reading Time Estimator leaves your app. Your Vinkius account keeps the connection and its history either way, ready for the next AI.

  • More questions about Reading Time Estimator? The Connector page answers them. See everything the Reading Time Estimator Connector can do

About the Connector

What Reading Time Estimator adds to your AI.

Once you connect it, your AI gains 3 capabilities: the exact actions it can take in Reading Time Estimator when you ask. The Connector page lists every one of them in detail, alongside the latency we measure in production and prompts worth trying first.

See everything the Reading Time Estimator Connector can do