Content
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🦊 MCPBench: A Benchmark for Evaluating MCP Servers
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[![Documentation][docs-image]][docs-url]
[![Package License][package-license-image]][package-license-url]
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[中文](https://github.com/modelscope/MCPBench/blob/main/README_zh.md) |
[English](https://github.com/modelscope/MCPBench/blob/main/README.md)
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MCPBench is an evaluation framework for MCP Servers. It supports the evaluation of three types of servers: Web Search, Database Query and GAIA, and is compatible with both local and remote MCP Servers. The framework primarily evaluates different MCP Servers (such as Brave Search, DuckDuckGo, etc.) in terms of task completion accuracy, latency, and token consumption under the same LLM and Agent configurations. Here is the [evaluation report](https://arxiv.org/abs/2504.11094).
<img src="assets/figure1.png" alt="MCPBench Overview" width="600"/>
> The implementation refers to [LangProBe: a Language Programs Benchmark](https://arxiv.org/abs/2502.20315).\
> Big thanks to Qingxu Fu for the initial implementation!
<hr>
# 📋 Table of Contents
- [🔥 News](#news)
- [🛠️ Installation](#installation)
- [🚀 Quick Start](#quick-start)
- [Launch MCP Server](#launch-mcp-server)
- [Launch Evaluation](#launch-evaluation)
- [🧂 Datasets and Experiments](#datasets-and-experiments)
- [🚰 Cite](#cite)
# 🔥 News
+ `Sep. 1, 2025` 🌟 Modelscope AI hackathon will be hold on Sep. 23rd, ref: https://modelscope.cn/active/aihackathon-mcp-agent
+ `Apr. 29, 2025` 🌟 Update the code for evaluating the MCP Server Package within GAIA.
+ `Apr. 14, 2025` 🌟 We are proud to announce that MCPBench is now open-sourced.
# 🛠️ Installation
The framework requires Python version >= 3.11, nodejs and jq.
```bash
conda create -n mcpbench python=3.11 -y
conda activate mcpbench
pip install -r requirements.txt
```
# 🚀 Quick Start
Please first determine the type of MCP server you want to use:
- If it is a remote host (accessed via **SSE**, such as [ModelScope](https://modelscope.cn/mcp), [Smithery](https://smithery.ai), or localhost), you can directly conduct the [evaluation](#launch-evaluation).
- If it is started locally (accessed via npx using **STDIO**), you need to launch it.
## Launch MCP Server (optional for stdio)
First, you need to write the following configuration:
```json
{
"mcp_pool": [
{
"name": "firecrawl",
"run_config": [
{
"command": "npx -y firecrawl-mcp",
"args": "FIRECRAWL_API_KEY=xxx",
"port": 8005
}
]
}
]
}
```
Save this config file in the `configs` folder and launch it using:
```bash
sh launch_mcps_as_sse.sh YOUR_CONFIG_FILE
```
For example, save the above configuration in the `configs/firecrawl.json` file and launch it using:
```bash
sh launch_mcps_as_sse.sh firecrawl.json
```
## Launch Evaluation
To evaluate the MCP Server's performance, you need to set up the necessary MCP Server information. the code will automatically detect the tools and parameters in the Server, so you don't need to configure them manually, like:
```json
{
"mcp_pool": [
{
"name": "Remote MCP example",
"url": "url from https://modelscope.cn/mcp or https://smithery.ai"
},
{
"name": "firecrawl (Local run example)",
"run_config": [
{
"command": "npx -y firecrawl-mcp",
"args": "FIRECRAWL_API_KEY=xxx",
"port": 8005
}
]
}
]
}
```
To evaluate the MCP Server's performance on WebSearch tasks:
```bash
sh evaluation_websearch.sh YOUR_CONFIG_FILE
```
To evaluate the MCP Server's performance on Database Query tasks:
```bash
sh evaluation_db.sh YOUR_CONFIG_FILE
```
To evaluate the MCP Server's performance on GAIA tasks:
```bash
sh evaluation_gaia.sh YOUR_CONFIG_FILE
```
For example, save the above configuration in the `configs/firecrawl.json` file and launch it using:
```bash
sh evaluation_websearch.sh firecrawl.json
```
# Datasets and Experimental Results
Our framework provides two datasets for evaluation. For the WebSearch task, the dataset is located at `MCPBench/langProBe/WebSearch/data/websearch_600.jsonl`, containing 200 QA pairs each from [Frames](https://arxiv.org/abs/2409.12941), news, and technology domains. Our framework for automatically constructing evaluation datasets will be open-sourced later.
For the Database Query task, the dataset is located at `MCPBench/langProBe/DB/data/car_bi.jsonl`. You can add your own dataset in the following format:
```json
{
"unique_id": "",
"Prompt": "",
"Answer": ""
}
```
We have evaluated mainstream MCP Servers on both tasks. For detailed experimental results, please refer to [Documentation](https://arxiv.org/abs/2504.11094)
# 🚰 Cite
If you find this work useful, please consider citing our project or giving us a 🌟:
```bibtex
@misc{mcpbench,
title={MCPBench: A Benchmark for Evaluating MCP Servers},
author={Zhiling Luo, Xiaorong Shi, Xuanrui Lin, Jinyang Gao},
howpublished = {\url{https://github.com/modelscope/MCPBench}},
year={2025}
}
```
Alternatively, you may reference our report.
```bibtex
@article{mcpbench_report,
title={Evaluation Report on MCP Servers},
author={Zhiling Luo, Xiaorong Shi, Xuanrui Lin, Jinyang Gao},
year={2025},
journal={arXiv preprint arXiv:2504.11094},
url={https://arxiv.org/abs/2504.11094},
primaryClass={cs.AI}
}
```
[docs-image]: https://img.shields.io/badge/Documentation-EB3ECC
[docs-url]: https://arxiv.org/abs/2504.11094
[package-license-image]: https://img.shields.io/badge/License-Apache_2.0-blue.svg
[package-license-url]: https://github.com/modelscope/MCPBench/blob/main/LICENSE
