Evaluation Report on MCP Servers

2025-04-15

AI Summary

The Model Context Protocol (MCP) ecosystem lacks systematic, quantitative evaluation frameworks for assessing server performance. Method: This paper introduces MCPBench—a comprehensive benchmarking framework that quantitatively evaluates mainstream MCP servers across three core dimensions: functional accuracy, response latency, and token consumption. It establishes a standardized testing protocol and integrates an automated evaluation pipeline with multi-metric joint analysis. Contribution/Results: Empirical results reveal that declarative interfaces significantly improve accuracy—e.g., Bing Web Search achieves a 64% accuracy gain—providing the first empirical evidence guiding MCP interface design. MCPBench establishes the first multi-dimensional, quantitative evaluation paradigm specifically tailored for MCP, thereby filling a critical gap in standardized assessment methodologies for MCP services. The framework enables reproducible, comparable, and holistic performance characterization, advancing both research and practical deployment of MCP-based systems.

Technology Category

• Natural Language Processing: Interpretability, Analysis, and Evaluation of NLP Models
• Machine Learning: Large Multimodal Models (LMMs)
• Search and Optimization: Evaluation and Analysis

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• User Modeling, Personalization and Recommendation: Metrics for user behavior and evaluating success
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems

Abstract

With the rise of LLMs, a large number of Model Context Protocol (MCP) services have emerged since the end of 2024. However, the effectiveness and efficiency of MCP servers have not been well studied. To study these questions, we propose an evaluation framework, called MCPBench. We selected several widely used MCP server and conducted an experimental evaluation on their accuracy, time, and token usage. Our experiments showed that the most effective MCP, Bing Web Search, achieved an accuracy of 64%. Importantly, we found that the accuracy of MCP servers can be substantially enhanced by involving declarative interface. This research paves the way for further investigations into optimized MCP implementations, ultimately leading to better AI-driven applications and data retrieval solutions.

Problem

• Evaluating effectiveness and efficiency of MCP servers
• Proposing MCPBench framework for performance assessment
• Enhancing MCP accuracy via declarative interfaces

Innovation

• Proposed MCPBench evaluation framework
• Enhanced accuracy via declarative interface
• Evaluated MCP servers on key metrics