🦊 MCPBench: A Benchmark for Evaluating MCP Servers

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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.

The implementation refers to LangProBe: a Language Programs Benchmark.
Big thanks to Qingxu Fu for the initial implementation!

📋 Table of Contents

• 🔥 News
• 🛠️ Installation
• 🚀 Quick Start
  • Launch MCP Server
  • Launch Evaluation
• 🧂 Datasets and Experiments
• 🚰 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.

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, Smithery, or localhost), you can directly conduct the 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:

{
    "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:

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:

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:

{
    "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
                }
            ]
        }  
    ]
}