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.

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.

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.

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.

How does this engine calculate user impact?

The calculate_error_metrics capability calculates impact by weighing the error rate against user visibility and the system's ability to recover via automatic retries.

Can I prioritize specific error types?

Yes, use analyze_reliability_priorities to rank error types based on their frequency and assigned severity weights.

What is the difference between error rate and effective error rate?

The error rate is the total frequency of failures, while the effective error rate specifically tracks errors that were neither recovered by the system nor hidden from the user.

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Connect AI Feature Error Metrics Engine to your AI

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. Understand user friction, not just raw error counts.

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.

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.

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 Error Metrics.

01
Analyze reliability priorities
Generates a strategic list of which error types to fix first
02
Calculate error metrics
Provides the fundamental error rate and user impact assessment
03
Get user experience health
Translates technical metrics into a qualitative assessment for product stakeholders
04
Summarize error distribution
Breaks down how errors are distributed across different categories

Real work

What teams actually run

Post-Launch Stability Check

After rolling out a new AI feature, run the metrics engine to immediately calculate the error rate and user impact score. This tells you if the feature is stable enough for a full release.

Prioritizing Tech Debt

When faced with dozens of potential fixes, use the reliability prioritization capability to get a data-backed list of which errors to tackle first.

Stakeholder Reporting

Instead of showing a spreadsheet of logs, use the user experience health capability to give executives a clear 'Critical' or 'Stable' status.

Debugging Hallucinations

If you suspect model hallucinations are causing issues, use the error distribution capability to confirm if that specific error type is spiking.

One connector, every AI

AI Feature Error Metrics 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 Feature Error Metrics 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

892ms average. Fast in production.

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

Daily averagePeak 1098ms

Oct 3Today

Fastest day: 712ms
Slowest day: 1098ms
14-day trend: Slowing+11%

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.

1In Claude Desktop, open Settings → Connectors.
2Click “Add custom connector” and paste the connector link above as the remote MCP server URL.
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

Guided setup for Claude? Set up AI Feature Error Metrics in Claude and other AIs

See all the AI clients this connector works with ↑

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.
05
How does this engine calculate user impact?
The calculate_error_metrics capability calculates impact by weighing the error rate against user visibility and the system's ability to recover via automatic retries.
06
Can I prioritize specific error types?
Yes, use analyze_reliability_priorities to rank error types based on their frequency and assigned severity weights.
07
What is the difference between error rate and effective error rate?
The error rate is the total frequency of failures, while the effective error rate specifically tracks errors that were neither recovered by the system nor hidden from the user.

Explore

More in Devops

Connect AI Feature Error Metrics to Claude, Cursor, ChatGPT & more

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

Each Connector runs inside its own sealed environment.

One switch stops every agent. Instantly.

Spending limits that pause instead of surprise.

We ship only the actions that matter.

01-04

Analyze reliability priorities

Calculate error metrics

Get user experience health

Summarize error distribution

What teams actually run

Post-Launch Stability Check

Prioritizing Tech Debt

Stakeholder Reporting

Debugging Hallucinations

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

AI Feature Error Metrics Connector

Claude Desktop

Product Manager

DevOps Engineer

AI Product Owner

Is this just a fancy error counter?

What kind of errors can it analyze?

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

Does this MCP work with all AI clients?

How does this engine calculate user impact?

Can I prioritize specific error types?

What is the difference between error rate and effective error rate?

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