A Taxonomy of Runtime Faults in Model Context Protocol Servers

arXiv:2606.05339 · cs.SE, cs.AI · Submitted 2026-06-03 · Read on arXiv

cs.SE, cs.AI

Submitted: 2026-06-03

Updated: 2026-09-19

Comments: 17 pages

Code: https://github.com/punkpeye/awesome-mcp-servers

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Model Context Protocol — 15× in this paper
• Benchmark — 4× in this paper · explained in 2 episodes
• Consistency — 4× in this paper · explained in 3 episodes
• Jheronimus Academy of Data Science — 4× in this paper
• Model Context Protocol (MCP) — 4× in this paper · explained in 8 episodes
• Abstraction — 3× in this paper · explained in 1 episode
• Verification — 3× in this paper · explained in 2 episodes
• Adam — 2× in this paper · explained in 2 episodes
• Artificial Intelligence — 2× in this paper
• Artificial Intelligence (AI) — 2× in this paper · explained in 2 episodes
• Deep Reinforcement Learning — 2× in this paper · explained in 2 episodes
• Foundation Model — 2× in this paper · explained in 3 episodes
• Foundation Models — 2× in this paper · explained in 7 episodes
• LLMs (Large Language Models) — 2× in this paper · explained in 2 episodes
• Reinforcement Learning — 2× in this paper · explained in 4 episodes
• Accuracy — 1× in this paper · explained in 1 episode
• Agentic Coding — 1× in this paper · explained in 1 episode
• Agentic System — 1× in this paper · explained in 2 episodes
• Benchmarking — 1× in this paper · explained in 3 episodes
• Downstream Tasks — 1× in this paper · explained in 2 episodes
• Empirical Coverage — 1× in this paper · explained in 2 episodes
• Failure Modes — 1× in this paper · explained in 3 episodes
• Generalizability — 1× in this paper · explained in 6 episodes
• Generalization — 1× in this paper · explained in 13 episodes
• Hallucination — 1× in this paper · explained in 3 episodes
• Hallucination Mitigation — 1× in this paper · explained in 2 episodes
• International Conference on Mining Software Repositories — 1× in this paper
• Iterative Refinement — 1× in this paper · explained in 3 episodes
• Large Language Models (LLMs) — 1× in this paper · explained in 6 episodes
• Robustness — 1× in this paper · explained in 5 episodes
• State Management — 1× in this paper · explained in 3 episodes
• Trace — 1× in this paper · explained in 2 episodes

Sources

• A Survey of Large Language Models
• On the Opportunities and Risks of Foundation Models
• A Comprehensive Survey of Hallucination Mitigation Techniques in Large Language Models
• Retrieval-Augmented Generation for Large Language Models: A Survey
• Model Context Protocol (MCP) at First Glance: Studying the Security and Maintainability of MCP Servers
• Systematization of Knowledge: Security and Safety in the Model Context Protocol Ecosystem
• A Measurement Study of Model Context Protocol Ecosystem
• MCPXKIT: The Unified Toolkit for Analyzing Model Context Protocol Security
• When MCP Servers Attack: Taxonomy, Feasibility, and Mitigation
• Model Context Protocol (MCP): Landscape, Security Threats, and Future Research Directions
• We Urgently Need Privilege Management in MCP: A Measurement of API Usage in MCP Ecosystems
• Parasites in the Toolchain: A Large-Scale Analysis of Attacks on the MCP Ecosystem
• On the Use of Agentic Coding: An Empirical Study of Pull Requests on GitHub
• muPRL: A Mutation Testing Pipeline for Deep Reinforcement Learning based on Real Faults
• Chaos Engineering in the Wild: Findings from GitHub
• Integrating Large Language Models in Software Engineering Education: A Pilot Study through GitHub Repositories Mining
• A First Look at the Self-Admitted Technical Debt in Test Code: Taxonomy and Detection

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