# Deterministic Agent Performance Scoring and Ranking. AI Agent Connect

> Agent Scoring & Ranking Engine provides a mathematical framework to evaluate multiple autonomous agents. It processes key performance metrics, including accuracy, latency, and cost, to generate precise composite scores and rankings. You can use this MCP to calculate normalized scores, find optimal performance trade-offs, and generate ordered lists of agents. It's built for rigorous agent evaluation workflows, giving you stable, reproducible results every time.

## Overview
- **Category:** analytics
- **Price:** Free
- **Endpoint:** https://edge.vinkius.com/vk_preview_pxy20XdHnsTrdCZB8wc0Ck1kM4p7yyqCZvVt0KgF/ai-agent-connect
- **Tags:** scoring, ranking, pareto, statistics, performance

## Description

When you're running multiple autonomous agents, knowing which one is best isn't just about looking at one number. You need a full picture. This MCP provides the math to evaluate agents against several metrics simultaneously. It takes performance data—like accuracy, how fast it runs, and how much it costs—and turns it into a single, reliable score. You can use it to find the best balance between conflicting goals, like achieving high accuracy without incurring massive latency. It's designed for deep analytics, giving you the solid, reproducible rankings you need to trust your AI architecture.

## Tools

### adjust_weights_for_correlation


### calculate_agent_scores
0

Calculates normalized composite scores and volatility for a set of agents

### identify_pareto_frontier


### rank_agents


## Prompt Examples

**Prompt:** 
```
Calculate the scores for these agents: [{'agentId': 'a1', 'accuracy': 0.9, 'latencyMs': 100, 'costPerCall': 0.01, 'availabilityPercent': 0.99, 'userSatisfaction': 0.8}] with weights {'accuracy': 0.5, 'latencyMs': 0.2, 'costPerCall': 0.1, 'availabilityPercent': 0.1, 'userSatisfaction': 0.1}
```

**Response:** 
```
The composite score for agent a1 is 0.85.
```

**Prompt:** 
```
Identify the Pareto frontier for these agents: [{'agentId': 'a1', 'accuracy': 0.9, 'latencyMs': 100}, {'agentId': 'a2', 'accuracy': 0.8, 'latencyMs': 200}]
```

**Response:** 
```
The Pareto frontier includes agent a1.
```

**Prompt:** 
```
Rank the top 2 agents using weighted_sum: [{'agentId': 'a1', 'compositeScore': 0.9}, {'agentId': 'a2', 'compositeScore': 0.7}, {'agentId': 'a3', 'compositeScore': 0.8}]
```

**Response:** 
```
The top 2 agents are a1 and a3.
```

## Capabilities

### Composite Scoring
The AI calculates a single, normalized score by weighting multiple metrics like accuracy, latency, and cost.

### Pareto Optimization
It identifies the Pareto frontier, showing the best possible trade-offs between conflicting performance goals.

### Agent Ranking
The AI generates clear, ordered lists, allowing you to quickly see which agents perform best overall.

### Bias Correction
It adjusts weights to prevent any single metric from unfairly dominating the final score.

### Volatility Analysis
The AI assesses the stability of the scores, helping you understand the reliability of the performance metrics.

## Use Cases

### 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.

## Benefits

- You get stable, reproducible rankings for agent evaluation, meaning your results won't change based on minor data shifts.
- The MCP handles complex metric normalization, allowing you to compare metrics with different units (like dollars vs. percentage) fairly.
- It prevents metric bias by allowing you to adjust weights, ensuring no single performance factor dictates the final outcome.
- You can visualize optimal trade-offs, helping you choose an agent that balances high performance with low operational cost.

## How It Works

Connecting this MCP is straightforward. Your AI client sends the performance metrics and desired weights, and the MCP returns the calculated scores and rankings.

1. Connect your AI client to the Vinkius catalog and select this MCP.
2. Provide the raw performance data, including metrics like accuracy, latency, and cost.
3. Specify the weights and metrics you want the MCP to prioritize.
4. The MCP processes the data and returns the normalized composite scores, rankings, or optimal trade-offs.

## Frequently Asked Questions

**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 tool 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.