# Evaluation Report on MCP Servers

arXiv: https://arxiv.org/abs/2504.11094 · HTML: https://mlsanity.com/paper/2504.11094/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Apr 2025.

## Built on by (most cited first, 9 of 9)

- [MCP-Zero: Active Tool Discovery for Autonomous LLM Agents](https://mlsanity.com/paper/2506.01056/) (2025)
- [MCPToolBench++: A Large Scale AI Agent Model Context Protocol MCP Tool Use Benchmark](https://mlsanity.com/paper/2508.07575/) (2025)
- [MCP-AgentBench: Evaluating Real-World Language Agent Performance with MCP-Mediated Tools](https://mlsanity.com/paper/2509.09734/) (2025)
- [LiveMCP-101: Stress Testing and Diagnosing MCP-enabled Agents on Challenging Queries](https://mlsanity.com/paper/2508.15760/) (2025)
- [MCP-Flow: Facilitating LLM Agents to Master Real-World, Diverse and Scaling MCP Tools](https://mlsanity.com/paper/2510.24284/) (2025)
- [TheMCPCompany: Creating General-purpose Agents with Task-specific Tools](https://mlsanity.com/paper/2510.19286/) (2025)
- [Help or Hurdle? Rethinking Model Context Protocol-Augmented Large Language Models](https://mlsanity.com/paper/2508.12566/) (2025)
- [AgentSelect: Benchmark for Narrative Query-to-Agent Recommendation](https://mlsanity.com/paper/2603.03761/) (2026)
- [Understanding How Enterprises Adopt the Model Context Protocol for LLM-Driven Software Engineering](https://mlsanity.com/paper/2606.09182/) (2026)

## Abstract (arXiv)

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.