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Use AI Feature Error Metrics with your AI.

Connect your account once and let the AI you already use work with it, without building another integration. Understand user friction, not just raw error counts.

Included with plan

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

Complete set · 4 capabilities

The complete AI Feature Error Metrics capability set.

These are the exact actions your AI can choose when you ask it to work with AI Feature Error Metrics.

Capability set01 / 01

01-04

4 capabilities in this set.

Part of 4 available through AI Feature Error Metrics.

  1. 01

    Analyze reliability priorities

    Generates a strategic list of which error types to fix first

  2. 02

    Calculate error metrics

    Provides the fundamental error rate and user impact assessment

  3. 03

    Get user experience health

    Translates technical metrics into a qualitative assessment for product stakeholders

  4. 04

    Summarize error distribution

    Breaks down how errors are distributed across different categories

Observed, not estimated

834ms average. Fast in production.

AI Feature Error Metrics is checked daily against the live service.

Daily averagePeak 973ms
Aug 28Today
Fastest day
773ms
Slowest day
973ms
14-day trend
Improving-9%

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 Error Metrics, so you can see the experience inside your AI.

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

AI Feature Error Metrics Connector

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

Connector linkhttps://edge.vinkius.com/vk_preview_LsvWKro1aPjHLDI8WFIOQItScUwetDJC8byNtVSJ/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 Error Metrics capabilities are ready to use.
Configuration · claude_desktop_config.jsonCopy
{
  "mcpServers": {
    "ai-feature-error-metrics-engine-mcp": {
      "url": "https://edge.vinkius.com/vk_preview_LsvWKro1aPjHLDI8WFIOQItScUwetDJC8byNtVSJ/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

Who it's for

Built for the work AI Feature Error Metrics owners hand off.

This MCP is essential for SaaS product teams and reliability engineers. If your job involves proving product stability or managing a complex AI feature, you need this. It lets you move past simple log analysis and quantify the actual user pain points.

  • 01

    Product Manager

    Use it to translate technical error data into clear, qualitative reports for executive stakeholders.

  • 02

    DevOps Engineer

    Use it to calculate core metrics and identify the most impactful error types for immediate remediation.

  • 03

    AI Product Owner

    Use it to strategically rank reliability efforts, ensuring you fix the errors that hurt the user the most.

FAQ

Questions AI Feature Error Metrics owners ask.

  • 01

    Is this just a fancy error counter?

    No. It goes much deeper than raw counts. It calculates the user impact score, which tells you how much friction the error causes for the end user, not just how many times it happened.

  • 02

    What kind of errors can it analyze?

    It handles common AI failures, such as Hallucinations or Model Timeouts, and helps you categorize them to understand their distribution across your user base.

  • 03

    Do I need to be a data scientist to use this?

    No. The capabilities are designed to take raw metrics and translate them into simple, actionable statuses, like 'Critical' or 'Stable,' which product managers can use immediately.

  • 04

    Does this MCP work with all AI clients?

    Yes. Since it's hosted on Vinkius, you connect once from any MCP-compatible client, including Claude, Cursor, and VS Code.

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