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Connect AI Model Adoption Velocity to ChatGPT and Claude

Set it up once and it works in ChatGPT, Claude, Cursor, and any other client you love. It all runs safely in the background, so you can relax and focus on what really matters. Predict market penetration and developer momentum.

Ask AI about this Connector

Developed, maintained, and hosted by Vinkius.

MCP VERIFIED · PRODUCTION READY · VINKIUS GUARANTEED

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

ChatGPTClaudeCursorPerplexityGeminiMicrosoft CopilotRaycastMeta AI

Your AI does the work. We keep it safe and sound.

No other AI tool gives you this. Your workflow runs in the client you already love, your account stays protected, and you decide what the agent can and cannot do. It is the whole reason to make the switch.

Each Connector lives inside our infrastructure, the same place where your workflow runs. The three things below are already part of it, working quietly in the background so you never have to think about them. Nothing to add, nothing to set up, nothing to worry about.

A futuristic MCP client docking bay
V8 Isolate01

Each Connector runs inside its own sealed environment.

Each Connector runs inside its own sealed V8 runtime — no access to your files, your system, or other servers. A Connector never sees past its own request.

Kill Switch02

One switch stops every agent. Instantly.

One action deactivates every Connector, revokes every token, and ends every session at once. AI Governance keeps the record.

FinOps per User03

Spending limits that pause instead of surprise.

Every user keeps their own connections and credentials, isolated from everyone else. Anything that would cross your spending limit is held for your approval, not billed.

Three controls, built into the layer your AI connects through.

Curated set · 4 capabilities

We ship only the actions that matter.

Capability set01 / 01

01-04

4 capabilities in this set.

Part of 4 available through AI Model Adoption Velocity.

  1. 01

    Get adoption velocity

    Calculates the core adoption velocity score for a specific model

  2. 02

    Get ecosystem impact

    Evaluates how community and developer infrastructure are accelerating or hindering adoption

  3. 03

    Get growth trajectory

    Predicts the future momentum and direction of model adoption

  4. 04

    Get viral metrics

    Specifically isolates the viral coefficient to understand organic growth potential

Real work

What teams actually run

01

Evaluating a New Model Launch

Before committing resources, run the MCP to see if a beta model has the necessary core adoption velocity score to justify a full rollout.

02

Competitive Analysis

Compare your model's ecosystem impact against a competitor's to pinpoint where your developer capabilities need improvement.

03

Investment Pitching

Use the growth trajectory data to show investors a clear, data-backed path to market dominance.

04

Resource Allocation

Determine which model requires immediate attention by checking its viral metrics for signs of stalled organic growth.

One connector, every AI

AI Model Adoption Velocity works with the most popular AI clients.

These are the most popular clients, each with a step-by-step guide: one link, set up once, with governance and visibility built in. And because everything runs on the MCP standard, the same connection also works in any other compatible client — nothing to rebuild.

ClaudeClaudeChatGPTChatGPTGeminiGeminiPerplexityPerplexityGrokGrokMicrosoft CopilotMicrosoft CopilotCursorCursorVS CodeVS CodeWindsurfWindsurfJetBrainsJetBrainsClineClineLangChainLangChainVercel AI SDKVercel AI SDKLovableLovableZ.aiZ.aiRaycastRaycastQwen CodeQwen CodeKimi CodeKimi CodeLe ChatLe Chat

Building your own app? The connector is yours to use.

You don't need a client to put AI Model Adoption Velocity to work: the same hosted connection plugs into your own applications and agent code, with the same governance on every request. Build with it, chat with it — one connection for both.

Observed, not estimated

878ms average. Fast in production.

AI Model Adoption Velocity is checked daily against the live service.

Daily averagePeak 1021ms
Oct 3Today
Fastest day
682ms
Slowest day
1021ms
14-day trend
Improving-5%

Connect your client

One URL. Every client.

Activate the Connector, copy your link, and paste it into the client you already use. 4 capabilities arrive ready to run.

Preview access · not provider authentication

The vk_preview_* token belongs to Vinkius preview infrastructure. It lets Claude discover and display the capabilities of AI Model Adoption Velocity, so you can see the experience inside your AI.

It does not authenticate your account with AI Model Adoption Velocity. Actions requiring credentials or live account data may not run until you activate the Connector and authorize the service.

AI Model Adoption Velocity Connector

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

Connector linkhttps://edge.vinkius.com/vk_preview_lyMrH9Yx17csa6rHitVMg74MkBlNXBs7iYLpo2kz/mcp

Claude Desktop

Follow the steps below to connect in seconds.

  1. 1In Claude Desktop, open Settings → Connectors.
  2. 2Click “Add custom connector” and paste the connector link above as the remote MCP server URL.
  3. 3Click Add and start a new chat — AI Model Adoption Velocity capabilities are ready to use.
Configuration · claude_desktop_config.jsonCopy
{
  "mcpServers": {
    "ai-model-adoption-velocity-mcp": {
      "url": "https://edge.vinkius.com/vk_preview_lyMrH9Yx17csa6rHitVMg74MkBlNXBs7iYLpo2kz/mcp"
    }
  }
}
  • Claude
  • ChatGPT
  • Cursor
  • VS Code
  • Windsurf
  • Claude Code
  • JetBrains
  • Cline

Step-by-step instructions for each client are in the guide. How to connect

Guided setup for Claude? Set up AI Model Adoption Velocity in Claude and other AIs

See all the AI clients this connector works with ↑

Who it's for

Built for the work AI Model Adoption Velocity owners hand off.

Product Managers, Strategy Analysts, and AI Architects use this MCP to move beyond gut feelings about product success. Instead of guessing, you get hard, quantifiable data on market adoption and developer interest. This helps you decide where to bet your next engineering cycle.

  • 01

    Product Manager

    Determines if a new feature or model has the necessary community pull to justify a full product launch.

  • 02

    Strategy Analyst

    Compares the adoption velocity of competing models to advise executive leadership on market positioning.

  • 03

    AI Architect

    Assesses the technical readiness and long-term viability of integrating a new model into an existing stack.

FAQ

Questions AI Model Adoption Velocity owners ask.

  • 01

    What kind of data does this MCP need?

    The MCP requires technical usage data, including current user counts, monthly active users, query growth rates, and an assessment of the model's integration depth.

  • 02

    Is this data real-time?

    The MCP processes current data points to calculate metrics. It provides a snapshot of the model's velocity and trajectory based on the inputs you provide.

  • 03

    Can I compare multiple models at once?

    You can run the MCP multiple times, feeding it the data for different models to compare their adoption velocity and ecosystem impact side-by-side.

  • 04

    What is the difference between adoption velocity and ecosystem impact?

    Adoption velocity measures the speed of usage. Ecosystem impact measures the quality of support—whether the community and developer capabilities are helping or slowing down that adoption.

  • 05

    What is the Adoption Velocity Score?

    The Adoption Velocity Score is a composite metric representing the speed of market penetration, synthesized from usage volume, growth intensity, and integration depth.

  • 06

    How can I predict future model growth?

    You can use the get_growth_trajectory capability to predict future momentum and direction based on current users and ecosystem strength.

  • 07

    Does this capability account for community engagement?

    Yes, the get_ecosystem_impact capability evaluates how community and developer infrastructure accelerate or hinder adoption.

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