Does this MCP handle different types of metrics?

Yes. It is designed to process diverse metrics like accuracy (a percentage), latency (milliseconds), and cost (currency). It normalizes them so you can compare them fairly in a single score.

What is the difference between scoring and ranking?

Scoring gives you a single, quantitative number for an agent's performance. Ranking takes those scores and puts them into a clear, ordered list, showing you the top performers.

Can I prevent one metric from dominating the score?

Absolutely. You use the capability to adjust weights for correlation, which prevents any single metric from unfairly skewing the final composite score.

What is the Pareto frontier?

It's a mathematical concept that finds the optimal trade-off. It shows you the set of agents where you can't improve one metric (like speed) without sacrificing another (like accuracy).

Is this MCP suitable for real-time scoring?

The MCP provides stable, reproducible rankings, making it ideal for batch evaluation workflows. The average latency is low, supporting frequent scoring runs.

How are agent scores calculated?

Scores are calculated by applying min-max normalization to each metric and multiplying the result by its assigned weight. For metrics where lower is better, like latency, the normalization is inverted.

Can I adjust weights if metrics are correlated?

Yes, you can use adjust_weights_for_correlation to detect statistical dependencies and receive suggested weight adjustments to prevent double-counting performance traits.

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Connect Agent Scoring Ranking Engine 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. Get stable, reproducible metrics for your autonomous systems.

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 Agent Scoring Engine.

01
Rank agents
02
Calculate agent scores
0 Calculates normalized composite scores and volatility for a set of agents
03
Identify pareto frontier
04
Adjust weights for correlation

Real work

What teams actually run

Model Selection Benchmarking

You have three agents for customer support. You use this MCP to score them based on accuracy, response time, and API cost to select the most efficient one.

Multi-Step Workflow Optimization

An automated workflow uses several agents in sequence. You use the MCP to score the entire system, identifying the weakest link in the chain.

Resource Constraint Analysis

You need an agent that is both highly accurate and extremely cheap to run. You use the Pareto frontier to find the best balance between these two conflicting goals.

A/B Testing Agent Performance

You are testing a new version of an agent. You use the MCP to calculate and compare its composite score against the stable production version.

One connector, every AI

Agent Scoring Engine 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 Agent Scoring Engine 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

970ms average. Fast in production.

Agent Scoring Engine is checked daily against the live service.

Daily averagePeak 1094ms

Oct 12Today

Fastest day: 961ms
Slowest day: 1094ms
14-day trend: Improving-12%

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 Agent Scoring Engine, so you can see the experience inside your AI.

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

Agent Scoring Engine Connector

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

Connector linkhttps://edge.vinkius.com/vk_preview_pxy20XdHnsTrdCZB8wc0Ck1kM4p7yyqCZvVt0KgF/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 — Agent Scoring Engine capabilities are ready to use.

Configuration · claude_desktop_config.jsonCopy

{
  "mcpServers": {
    "agent-scoring-ranking-engine-mcp": {
      "url": "https://edge.vinkius.com/vk_preview_pxy20XdHnsTrdCZB8wc0Ck1kM4p7yyqCZvVt0KgF/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 Scoring Engine in Claude and other AIs

See all the AI clients this connector works with ↑

Who it's for

Built for the work Agent Scoring Engine owners hand off.

This MCP is essential for ML Engineers, AI Architects, and Data Scientists who manage multiple autonomous agents. If you need to compare several AI models or agents and determine which one is truly optimal, this capability gives you the mathematical backing you need. It moves you past simple averages and into deep performance analytics.

01
ML Engineer
Uses the MCP to calculate composite scores and understand the trade-offs between accuracy and latency for model deployment.
02
AI Architect
Determines the optimal agent combination by identifying the Pareto frontier across multiple system components.
03
Data Scientist
Applies the scoring and ranking capabilities to benchmark different agent versions against established performance standards.

FAQ

Questions Agent Scoring Engine owners ask.

01
Does this MCP handle different types of metrics?
Yes. It is designed to process diverse metrics like accuracy (a percentage), latency (milliseconds), and cost (currency). It normalizes them so you can compare them fairly in a single score.
02
What is the difference between scoring and ranking?
Scoring gives you a single, quantitative number for an agent's performance. Ranking takes those scores and puts them into a clear, ordered list, showing you the top performers.
03
Can I prevent one metric from dominating the score?
Absolutely. You use the capability to adjust weights for correlation, which prevents any single metric from unfairly skewing the final composite score.
04
What is the Pareto frontier?
It's a mathematical concept that finds the optimal trade-off. It shows you the set of agents where you can't improve one metric (like speed) without sacrificing another (like accuracy).
05
Is this MCP suitable for real-time scoring?
The MCP provides stable, reproducible rankings, making it ideal for batch evaluation workflows. The average latency is low, supporting frequent scoring runs.
06
How are agent scores calculated?
Scores are calculated by applying min-max normalization to each metric and multiplying the result by its assigned weight. For metrics where lower is better, like latency, the normalization is inverted.
07
Can I adjust weights if metrics are correlated?
Yes, you can use adjust_weights_for_correlation to detect statistical dependencies and receive suggested weight adjustments to prevent double-counting performance traits.

Explore

More in Analytics

Connect Agent Scoring Engine 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

Rank agents

Calculate agent scores

Identify pareto frontier

Adjust weights for correlation

What teams actually run

Model Selection Benchmarking

Multi-Step Workflow Optimization

Resource Constraint Analysis

A/B Testing Agent Performance

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

Agent Scoring Engine Connector

Claude Desktop

ML Engineer

AI Architect

Data Scientist

Does this MCP handle different types of metrics?

What is the difference between scoring and ranking?

Can I prevent one metric from dominating the score?

What is the Pareto frontier?

Is this MCP suitable for real-time scoring?

How are agent scores calculated?

Can I adjust weights if metrics are correlated?

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