MCPZoo: A Large-Scale Dataset of Runnable Model Context Protocol Servers for AI Agent
Mengying Wu, Pei Chen, Geng Hong, Baichao An, Jinsong Chen, Binwang Wan, Xudong Pan, Jiarun Dai, Min Yang

TL;DR
MCPZoo is a large, open dataset of over 129,000 Model Context Protocol servers, including verified runnable instances, designed to facilitate research on agent-tool interactions and security analysis.
Contribution
This work introduces MCPZoo, the largest comprehensive dataset of MCP servers with verified runnable instances, enabling realistic experimentation and systematic exploration.
Findings
Contains 129,059 servers with 56,053 distinct entries
Includes 16,356 verified, interactable server instances
Supports research on MCP systems and security analysis
Abstract
Model Context Protocol (MCP) enables agents to interact with external tools, yet empirical research on MCP is hindered by the lack of large-scale, accessible datasets. We present MCPZoo, the largest and most comprehensive dataset of MCP servers collected from multiple public sources, comprising 129,059 servers (56,053 distinct). MCPZoo includes 16,356 server instances that have been deployed and verified as runnable and interactable, supporting realistic experimentation beyond static analysis. The dataset provides unified metadata and access interfaces, enabling systematic exploration and interaction without manual deployment effort. MCPZoo is released as an open and accessible resource to support research on MCP-based systems and security analysis.
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Taxonomy
TopicsMobile Agent-Based Network Management · Access Control and Trust · Context-Aware Activity Recognition Systems
