A Large-Scale Evolvable Dataset for Model Context Protocol Ecosystem and Security Analysis

2025-06-29

AI Summary

The Model Context Protocol (MCP) ecosystem lacks systematic, empirical resources for rigorous analysis and evaluation. Method: This paper introduces MCPCorpus—the first large-scale, extensible dataset of MCP components, comprising ~14,000 servers and 300 clients. It employs automated crawling, structural normalization, metadata annotation, and GitHub activity analysis to standardize protocol implementations; it also provides supporting tools for automatic synchronization, consistency validation, and lightweight web-based retrieval to enable dynamic updates and longitudinal ecosystem studies. Contribution/Results: MCPCorpus establishes the first comprehensive, reproducible observational baseline for the MCP ecosystem. It significantly enhances quantitative analysis of protocol adoption trends, implementation diversity, and ecosystem health. Moreover, it serves as critical empirical infrastructure for security auditing, interoperability assessment, and standards evolution—thereby advancing evidence-driven development and governance of MCP.

Technology Category

• Multiagent Systems: Modeling other Agents
• Machine Learning: Scalability of ML Systems
• Cognitive Modeling & Cognitive Systems: Agent Architectures

Application Category

• Security and Privacy: Large-scale security measurements
• Systems and Infrastructure for Web, Mobile and WoT: Web performance, measurement, and characterization
• User Modeling, Personalization and Recommendation: Practical large-scale studies of user experience

Abstract

The Model Context Protocol (MCP) has recently emerged as a standardized interface for connecting language models with external tools and data. As the ecosystem rapidly expands, the lack of a structured, comprehensive view of existing MCP artifacts presents challenges for research. To bridge this gap, we introduce MCPCorpus, a large-scale dataset containing around 14K MCP servers and 300 MCP clients. Each artifact is annotated with 20+ normalized attributes capturing its identity, interface configuration, GitHub activity, and metadata. MCPCorpus provides a reproducible snapshot of the real-world MCP ecosystem, enabling studies of adoption trends, ecosystem health, and implementation diversity. To keep pace with the rapid evolution of the MCP ecosystem, we provide utility tools for automated data synchronization, normalization, and inspection. Furthermore, to support efficient exploration and exploitation, we release a lightweight web-based search interface. MCPCorpus is publicly available at: https://github.com/Snakinya/MCPCorpus.

Problem

• Lack of structured view of MCP artifacts for research
• Need for dataset to study MCP ecosystem trends and health
• Challenges in tracking rapid evolution of MCP ecosystem

Innovation

• Large-scale dataset with 14K MCP servers
• Automated tools for data synchronization
• Web-based search interface for exploration