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Connect AI Feature Value Realization 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. Pinpoint adoption roadblocks and accelerate business impact.

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 Feature Value Realization.

  1. 01

    Analyze complexity impact

    Determines how much the complexity of a specific use case will naturally extend the expected time to value

  2. 02

    Calculate ttv metrics

    Calculates the primary performance indicators for AI feature value delivery

  3. 03

    Get acceleration recommendations

    Suggests specific ways to shorten the time to value based on current performance

  4. 04

    Validate milestone readiness

    Checks if a user is on track to reach a milestone based on their current interaction history

Real work

What teams actually run

01

Onboarding Flow Audit

You suspect new users are getting lost in the setup process. Run a TTV analysis to see exactly where the drop-off happens and how long it takes them to complete the first key action.

02

Feature Launch Readiness

Before rolling out a complex feature, you use the complexity impact capability to predict if the current user base can handle it without significant delay.

03

Identifying Bottlenecks

Your usage data is high, but adoption isn't. Use the acceleration recommendations capability to get immediate, actionable steps to boost usage.

04

Enterprise Adoption Tracking

For large accounts, you validate if key users are on track to hit their quarterly success milestones, ensuring renewal readiness.

One connector, every AI

AI Feature Value Realization 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 Feature Value Realization 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

909ms average. Fast in production.

AI Feature Value Realization is checked daily against the live service.

Daily averagePeak 1109ms
Oct 3Today
Fastest day
663ms
Slowest day
1109ms
14-day trend
Slowing+7%

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 Feature Value Realization, so you can see the experience inside your AI.

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

AI Feature Value Realization Connector

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

Connector linkhttps://edge.vinkius.com/vk_preview_eUP7CnBJLcUPjEin2ay7Hfjg5YLybRikVFub3HZg/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 Feature Value Realization capabilities are ready to use.
Configuration · claude_desktop_config.jsonCopy
{
  "mcpServers": {
    "ai-feature-value-realization-mcp": {
      "url": "https://edge.vinkius.com/vk_preview_eUP7CnBJLcUPjEin2ay7Hfjg5YLybRikVFub3HZg/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 Feature Value Realization in Claude and other AIs

See all the AI clients this connector works with ↑

Who it's for

Built for the work AI Feature Value Realization owners hand off.

If you're building a SaaS product and need to prove that your AI features actually drive business results, this MCP is for you. It helps product teams move past vanity metrics and focus on true user adoption and ROI.

  • 01

    Product Manager

    Use it to identify which features are failing to gain traction and where to focus your next development sprint.

  • 02

    Data Analyst

    Use it to calculate and compare Time to Value (TTV) across different user segments and complexity levels.

  • 03

    Product Owner

    Use it to validate if a user base is ready for a major feature release or if they need more guided onboarding.

FAQ

Questions AI Feature Value Realization owners ask.

  • 01

    What is Time to Value (TTV) in this context?

    TTV measures how quickly a user realizes the core business benefit from your AI feature. It's not just about sign-ups; it's about when they achieve measurable success using the capability.

  • 02

    Does this MCP require me to store my data on Vinkius?

    No. You connect your AI client to Vinkius, and the MCP processes the data you provide to calculate the metrics. Vinkius hosts the MCP, but it doesn't own your raw interaction data.

  • 03

    How do I know if a feature is too complex?

    You use the analyze_complexity_impact capability. It determines how much the difficulty of a specific use case naturally extends the expected time to value, giving you a clear warning.

  • 04

    Can I use this for multiple user types?

    Yes. The capabilities allow you to segment data by user type, letting you compare the TTV and adoption rates between standard users, power users, and executives.

  • 05

    What kind of data does this MCP need?

    It requires AI interaction data, including timestamps of first use, milestone dates, and confirmation rates, to calculate accurate performance indicators.

  • 06

    How is Time to Value (TTV) calculated?

    TTV is calculated as the number of days between the first recorded AI interaction and the date the predefined value milestone is achieved using calculate_ttv_metrics.

  • 07

    Can I get specific advice to improve my AI feature adoption?

    Yes, you can use get_acceleration_recommendations to receive tailored strategies based on your current TTV, user segment, and use case complexity.

  • 08

    How does complexity affect my metrics?

    Complexity acts as a multiplier. You can use analyze_complexity_impact to see how high-complexity tasks naturally extend the expected time to value for different user segments.

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