mlsanityData to 14 Oct 2026

MCP Error Messages Written for Developers Hurt the Most Capable Agents Most

arXiv 2609.35381 ↗First posted 28 Sep 2026cs.SE cs.AIcited by 0

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Abstract (arXiv)

Many Model Context Protocol (MCP) servers wrap web APIs built for human developers, and their error messages tell the reader to run a command, edit a configuration, open a web page or wait. Many agents that read them can only call the server's tools. In 150 widely used MCP servers, 949 of 3,001 error messages tell the caller what to do next, and half of these steps depend on something the server cannot see about the caller. On credential errors, 62 of 67 steps ask for a terminal command, a configuration change or a web page; on rate limits, 20 of 30 say to wait and retry without naming the call to repeat. We tested five OpenAI models that act only through the tools of Berkeley Function Calling Leaderboard tasks, and the agents did what the step said. On expired credentials, a terminal command in the step left 45% of tasks recovered, and the loss it caused grew from 18 points for GPT-5.5 to 69 for GPT-6 Astra. On a rate limit, GitHub's "Wait before retrying." left 6%. We tested two remedies. For MCP developers, naming a server tool in the step raised recovery on expired credentials to 84%, with the login tool in place of the command, and on a rate limit to 88%, with the call to repeat in place of the bare wait. For agent developers, deleting the step with a one-sentence prompt before the model reads it raised recovery on expired credentials to 82%.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 12 of its 23 references that are in the map. First posted on arXiv 28 Sep 2026.

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12 of 23 references.

References · 12

  1. 2023Large Language Models Cannot Self-Correct Reasoning Yetcited by 1,017
  2. 2023CRITIC: Large Language Models Can Self-Correct with Tool-Interactive Critiquingcited by 783
  3. 2024InjecAgent: Benchmarking Indirect Prompt Injections in Tool-Integrated Large Language Model Agentscited by 538
  4. 2026Model Context Protocol (MCP) Tool Descriptions Are Smelly! Towards Improving AI Agent Efficiency with Augmented MCP Tool Descriptionscited by 17
  5. 2025From REST to MCP: An Empirical Study of API Wrapping and Automated Server Generation for LLM Agentscited by 14
  6. 2025Failure Makes the Agent Stronger: Enhancing Accuracy through Structured Reflection for Reliable Tool Interactionscited by 12
  7. 2026Real Faults in Model Context Protocol (MCP) Software: a Comprehensive Taxonomycited by 6
  8. 2026ToolMisuseBench: An Offline Deterministic Benchmark for Tool Misuse and Recovery in Agentic Systemscited by 4
  9. 2026Type-Error Ablation and AI Coding Agentscited by 2
  10. 2026ToolRobustBench: Stage-Wise Perturbation Evaluation and Failure Diagnosis for Tool-Calling Agentscited by 2
  11. 2026When the Manual Lies: A Realistic Benchmark to Evaluate MCP Poisoning Attacks for LLM Agentscited by 1
  12. 2026The Curse of Helpfulness: Inverse Scaling Law in Robustness to Distractor Instructions via DistractionIFcited by 1

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  4. 2026A Taxonomy of Runtime Faults in Model Context Protocol Servers
  5. 2026Model Context Protocol Threat Modeling and Analyzing Vulnerabilities to Prompt Injection with Tool Poisoning
  6. 2026Same Name, Different Server: A Security Census of Silent Drift in the Model Context Protocol Ecosystem
  7. 2025Securing the Model Context Protocol (MCP): Risks, Controls, and Governance
  8. 2026Can MCP Clients Decide What to Do After Failure? A Result-Only Actionability Audit ↗