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Connect Agent Evaluation Metrics Calculator 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. Stop guessing. Get hard data on accuracy and efficiency.

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 · 3 capabilities

We ship only the actions that matter.

Capability set01 / 01

01-03

3 capabilities in this set.

Part of 3 available through Agent Evaluation Metrics Calculator.

  1. 01

    Calculate composite and health

    Generates a single performance score and determines if the agent is underperforming relative to a baseline

  2. 02

    Calculate efficiency metrics

    Analyzes the operational cost and speed of the agent

  3. 03

    Calculate core metrics

    Calculates the foundational performance indicators (accuracy, precision, recall, and F1 score) based on task outcomes

Real work

What teams actually run

01

A/B Testing Agent Versions

Compare two different agent versions by running both through the MCP to see which one achieves a higher composite score and better efficiency.

02

Monitoring Production Drift

Set a baseline score for your live agent. Periodically run the MCP to check if the current performance has dropped, signaling a need for retraining.

03

Budgeting for API Calls

Estimate the total cost of a feature by running sample tasks through the efficiency metrics capability to calculate expected token usage and latency.

04

Defining Success Criteria

Instead of saying 'it should be good,' you use this MCP to define success as 'maintaining an F1 score above 0.85 and a composite score above 0.6.'

One connector, every AI

Agent Evaluation Metrics Calculator 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 Agent Evaluation Metrics Calculator 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

875ms average. Fast in production.

Agent Evaluation Metrics Calculator is checked daily against the live service.

Daily averagePeak 1088ms
Oct 3Today
Fastest day
670ms
Slowest day
1088ms
14-day trend
Slowing+17%

Connect your client

One URL. Every client.

Activate the Connector, copy your link, and paste it into the client you already use. 3 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 Agent Evaluation Metrics Calculator, so you can see the experience inside your AI.

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

Agent Evaluation Metrics Calculator Connector

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

Connector linkhttps://edge.vinkius.com/vk_preview_U0L7ecHOaTRSDq7nAgL5eY45DyQTMS1ZlC0QwKTz/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 — Agent Evaluation Metrics Calculator capabilities are ready to use.
Configuration · claude_desktop_config.jsonCopy
{
  "mcpServers": {
    "agent-evaluation-metrics-calculator-mcp": {
      "url": "https://edge.vinkius.com/vk_preview_U0L7ecHOaTRSDq7nAgL5eY45DyQTMS1ZlC0QwKTz/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 Agent Evaluation Metrics Calculator in Claude and other AIs

See all the AI clients this connector works with ↑

Who it's for

Built for the work Agent Evaluation Metrics Calculator owners hand off.

This MCP is built for teams that need to move beyond anecdotal testing. If you build AI agents and need to prove their reliability, cost-effectiveness, and consistency, this capability gives you the objective data to back up your claims.

  • 01

    ML Engineer

    Use this to build automated testing pipelines that track model drift and performance degradation over time.

  • 02

    Data Scientist

    Run deterministic evaluations to compare multiple agent versions against a single, quantifiable benchmark.

  • 03

    AI Product Manager

    Get clear metrics on the trade-offs between agent performance, speed, and API costs before launching a feature.

FAQ

Questions Agent Evaluation Metrics Calculator owners ask.

  • 01

    What kind of data do I need to run this MCP?

    You need structured data about the agent's performance. Specifically, you must provide task outcomes (expected vs. actual), along with operational metrics like average latency and tokens used.

  • 02

    Is this just a dashboard for my agent's scores?

    No, this is a functional MCP. You connect it to your agent, and your AI client runs the calculations for you. It's a deterministic engine, not just a viewing portal.

  • 03

    Does this compare my agent to other models?

    It doesn't compare your agent to other models. It measures your agent's performance against a defined baseline or against its own historical best performance.

  • 04

    What is the difference between the composite score and the core metrics?

    Core metrics (like accuracy) are single indicators. The composite score is a weighted average that combines accuracy, latency, and efficiency into one single number, giving you a holistic health check.

  • 05

    Can I use this for non-AI tasks?

    No. This MCP is specifically designed for evaluating the performance, cost, and reliability of AI agents and models.

  • 06

    How do I calculate the F1 score?

    You can use the calculate_core_metrics capability by providing an array of task results. It will return the F1 score along with accuracy, precision, and recall.

  • 07

    What triggers a regression flag?

    A regression flag is triggered via calculate_composite_and_health if the current accuracy falls below 95% of your specified baseline accuracy.

  • 08

    How is token efficiency measured?

    Token efficiency is calculated by the calculate_efficiency_metrics capability as the ratio of successful tasks to the total number of tokens consumed.

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