arXiv: cs.AI preprintsInternational6 November 2026
MCPHallu: Benchmarking Reasoning, Execution, and Memory Hallucinations in MCP Agents
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arXiv:2611.07689v1 Announce Type: new Abstract: Large language model agents are increasingly deployed through the Model Context Protocol (MCP), which allows them to discover and invoke tools across multiple servers at runtime. Existing MCP benchmarks primarily measure task completion; where they diagnose failures, they do so after execution rather than building specific reasoning, execution, and memory failure risks into task construction. We introduce MCPHallu, a benchmark for diagnosing hallucination-prone agent failures in MCP environments along three axes: reasoning, execution, and memory.
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