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Connect AI Explainability Economics 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. Determine if XAI is viable for your business goals.

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 Explainability Economics.

  1. 01

    Calculate infrastructure expenditure

    This capability determines the direct additional cost of hardware and storage needed to support XAI, giving you a clear picture of the technical overhead.

  2. 02

    Calculate trust roi

    Use this capability to quantify the business value gained from increased user confidence, helping you measure retention gains in dollars.

  3. 03

    Estimate compliance benefit

    This function calculates the theoretical value of avoiding regulatory fines and legal friction, turning legal risk into a quantifiable asset.

  4. 04

    Generate economic summary

    Finally, this capability provides a consolidated view of the total cost versus the total benefit, giving you the final net economic impact assessment.

Real work

What teams actually run

01

Launching a Financial Model

Before deploying a new lending model, you use this MCP to calculate the compliance benefit and infrastructure cost, proving the model is both safe and affordable.

02

Improving Customer Trust

You run a simulation to calculate the trust ROI, showing executives that the investment in transparency will lead to higher customer lifetime value.

03

Regulatory Audit Preparation

You use the MCP to model the financial impact of adopting XAI, preparing a solid, data-driven defense for the model's governance.

04

Budgeting for AI Infrastructure

You determine the total expenditure for XAI, ensuring the project's technical requirements don't exceed the allocated budget.

One connector, every AI

AI Explainability Economics 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.

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

You don't need a client to put AI Explainability Economics 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

990ms average. Fast in production.

AI Explainability Economics is checked daily against the live service.

Daily averagePeak 1053ms
Oct 14Today
Fastest day
951ms
Slowest day
1053ms
14-day trend
Improving-6%

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 Explainability Economics, so you can see the experience inside your AI.

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

AI Explainability Economics Connector

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

Connector linkhttps://edge.vinkius.com/vk_preview_eSADUUJD8NLb9LIphuUPgxdZOpWEYuM40fhp1OCn/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 Explainability Economics capabilities are ready to use.
Configuration · claude_desktop_config.jsonCopy
{
  "mcpServers": {
    "ai-explainability-economics-mcp": {
      "url": "https://edge.vinkius.com/vk_preview_eSADUUJD8NLb9LIphuUPgxdZOpWEYuM40fhp1OCn/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 Explainability Economics in Claude and other AIs

See all the AI clients this connector works with ↑

Who it's for

Built for the work AI Explainability Economics owners hand off.

This MCP is essential for Product Managers, Risk Officers, and CTOs who need to justify AI initiatives with hard numbers. If you're responsible for model governance or launching AI in a regulated industry, you need this capability. It translates technical debt into financial risk and opportunity.

  • 01

    Product Manager

    Use this MCP to prove the ROI of XAI to stakeholders, moving the discussion from 'should we' to 'how much will it cost/earn'.

  • 02

    Risk Officer

    Run compliance benefit calculations to quantify the financial risk reduction achieved by implementing explainability features.

  • 03

    CTO/VP of Engineering

    Determine the total infrastructure expenditure, ensuring the proposed XAI solution fits within the current budget and hardware capacity.

FAQ

Questions AI Explainability Economics owners ask.

  • 01

    Does this MCP calculate the cost of XAI features?

    Yes. You use the calculate_infrastructure_expenditure capability to determine the direct additional cost of hardware and storage needed to support XAI. It helps you budget for the technical overhead.

  • 02

    What kind of ROI can I calculate?

    You can quantify the business value gained from increased user confidence using calculate_trust_roi. This measures the financial lift from higher user retention rates.

  • 03

    Is this for regulated industries only?

    While it's critical for regulated fields like finance and healthcare, it's useful anytime you need to prove the financial viability of a complex AI feature to stakeholders.

  • 04

    How do I get a final summary?

    You run the generate_economic_summary capability. This provides a consolidated view, balancing the total cost against the total benefit in one place.

  • 05

    What does 'compliance benefit' mean?

    The estimate_compliance_benefit capability calculates the theoretical value of avoiding regulatory fines and legal friction. It helps you assign a dollar value to risk mitigation.

  • 06

    How do I calculate the total cost of adding explainability?

    You can use the calculate_infrastructure_expenditure capability, which takes into account the explanation method, compute overhead, storage per explanation, and total inferences.

  • 07

    Can I determine if an XAI project is financially viable?

    Yes, by using generate_economic_summary, you can compare the total costs against the compliance value and trust ROI to see if the net economic impact is positive.

  • 08

    What factors influence the compliance benefit?

    The estimate_compliance_benefit capability calculates this based on the customer requirement percentage, the complexity of the explanation method, and the baseline risk exposure.

Connect AI Explainability Economics to Claude, Cursor, ChatGPT & more