GitHub 上 MCP 实现的大规模数据集
A Large-Scale Dataset of MCP Implementations on GitHub
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中文总结 AI 辅助
该研究针对模型上下文协议（MCP）在开源开发中的应用情况，通过混合管道从 GitHub 收集并验证相关数据，构建了大规模数据集，分析得出 Python 和 TypeScript 主导 MCP 开发且混合架构最常见，为 MCP 生态系统研究建立基准并支持相关未来研究。
中文摘要 AI 辅助
模型上下文协议（MCP）的迅速出现为连接大语言模型与外部工具和服务引入了新标准。尽管它在开源开发中迅速被采用，但对 MCP 如何实现、构建和维护的系统理解仍然有限。本研究展示了首个直接从 GitHub 收集的基于证据的大规模真实世界 MCP 实现数据集。通过将 GitHub REST 和 GraphQL API 与自定义 Python 验证脚本集成的混合管道，发现、筛选并验证了 3238 个候选存储库。每个经过验证的项目按操作角色分类，并以可重现的 JSONL 模式导出。对一个代表性子集的人工审查在 95%置信水平下确认总体精度为 83%，还发现了一些主要用作教育样本、教程或演示模板的存储库。应用有针对性的排除规则去除这些非操作性存储库，得到了包含 2297 个经过验证的 MCP 项目的最终数据集。分析表明 Python 和 TypeScript 在 MCP 开发中占主导地位，混合架构是最常见的设计模式。这项工作通过强调透明验证策略、结构化证据标记和可重现的数据组织，为研究真实世界的 MCP 生态系统建立了基础基准，并支持更广泛开发者社区未来在集成、连接性和兼容性方面的研究。
英文摘要
The rapid emergence of the Model Context Protocol (MCP) has introduced a new standard for connecting large language models to external tools and services. Despite its rapid adoption in open-source development, systematic understanding of how MCP is implemented, structured, and maintained remains limited. This study presents the first large-scale, evidence-based dataset of real-world MCP implementation collected directly from GitHub. Using a hybrid pipeline that integrates the GitHub REST and GraphQL APIs with custom Python verification scripts, 3,238 candidate repositories were discovered, filtered, and validated through multi-stage evidence checks. Each verified project was classified by operational role (e.g., client, server, gateway) and exported in a reproducible JSONL schema. A manual review of a representative subset confirmed an overall precision of 83% at a 95% confidence level, and additionally revealed a set of repositories functioning primarily as educational samples, tutorials, or demonstration templates. A targeted exclusion rule was then applied to remove these non-operational repositories, resulting in a final dataset of 2,297 validated MCP projects. The analysis shows that Python and TypeScript dominate MCP development, with hybrid architectures emerging as the most common design pattern. By emphasizing transparent verification strategies, structured evidence tagging, and reproducible data organization, this work establishes a foundational benchmark for studying real-world MCP ecosystems and supports future research on integration, connectivity, and compatibility across the broader developer community.
发表机构
- Oakland University（奥克兰大学）
- University of North Carolina Wilmington（北卡罗来纳州温斯洛普大学）
机构由 AI 辅助整理，请以论文原文为准。
