MCPBench — MCP Server by modelscope

by modelscope · MCP Server · ★ 241

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About MCPBench

🦊 MCPBench: A Benchmark for Evaluating MCP Servers [![Documentation][docs-image]][docs-url] [![Package License][package-license-image]][package-license-url] 中文 | English 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 🌟 Modelscope AI hackathon will be hold on Sep. 23r

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Quick Facts

Stars241
Forks15
LanguagePython
CategoryMCP Server
LicenseApache-2.0
Quality Score40.2/100
Open Issues6
Last Updated2025-09-03
Created2025-04-13
Platformsbrowser, mcp, python
Est. Tokens~6634k

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Frequently Asked Questions

What is MCPBench?

MCPBench is The evaluation benchmark on MCP servers. It is categorized as a MCP Server with 241 GitHub stars.

What programming language is MCPBench written in?

MCPBench is primarily written in Python. It covers topics such as benchmark, database, mcp.

How do I install or use MCPBench?

You can find installation instructions and usage details in the MCPBench GitHub repository at github.com/modelscope/MCPBench. The project has 241 stars and 15 forks, indicating an active community.

What license does MCPBench use?

MCPBench is released under the Apache-2.0 license, making it free to use and modify according to the license terms.

What are the best alternatives to MCPBench?

The top alternatives to MCPBench on Agent Skills Hub include mcp-neo4j, mcp-redis, pgmcp. Each offers a different approach to the same problem space — compare them side-by-side by stars, quality score, and community activity.

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