Agent Scoring & Ranking Engine

Agent Scoring & Ranking Engine MCP Connector for Claude

A+

Deterministic performance scoring and ranking for autonomous agents.

4 tools Official Updated Oct 1, 2026 Official Vinkius Partner

This MCP server provides a mathematical framework for evaluating autonomous agents. It processes performance metrics like accuracy, latency, and cost to generate precise composite scores. Use calculate_agent_scores to normalize metrics and compute scores, identify_pareto_frontier to find optimal trade-offs, rank_agents to generate ordered lists, and adjust_weights_for_correlation to prevent metric bias. It is designed to provide stable, reproducible rankings for agent evaluation workflows.

scoringrankingparetostatisticsperformance

4 tools expose this connector's capabilities to your AI agent.

rank_agents

calculate_agent_scores

0 Calculates normalized composite scores and volatility for a set of agents

identify_pareto_frontier

adjust_weights_for_correlation

See how to talk to your AI agent using Agent Scoring & Ranking Engine.

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}

The composite score for agent a1 is 0.85.

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

The Pareto frontier includes agent a1.

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

The top 2 agents are a1 and a3.

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.

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