arXiv:2606.05339v1 Announce Type: cross Abstract: MCP (Model Context Protocol) enables LLMs (Large Language Models) to interact with external tools and data sources via a standardized protocol. Its rapid adoption in tool-augmented Artificial Intelligence (AI) workflows has introduced new reliability challenges, such as configuration parameters that are accepted but not enforced at runtime, leading to unintended default behavior, whose runtime fault characteristics remain empirically unexamined. We present the first empirical taxonomy of runtime faults in MCP servers. We manually analyzed 837 M
The rapid adoption of Model Context Protocol (MCP) in AI workflows has brought reliability challenges to the forefront, necessitating empirical examination of runtime faults.
This research provides a foundational understanding of critical failure modes in AI systems, which is essential for building robust and trustworthy large language model applications.
The empirical taxonomy of runtime faults in MCP servers now provides developers and researchers with specific insights into current vulnerabilities, enabling more targeted development and more robust system design.
- · AI developers
- · Cloud infrastructure providers
- · Cybersecurity firms
- · Companies with unreliable AI deployments
- · Users of unstable AI applications
Improved reliability and stability of AI applications leveraging MCP.
Increased trust and adoption of advanced AI tools in critical enterprise environments.
New industry standards and best practices emerging for AI system reliability and fault tolerance.
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Read at arXiv cs.AI