🤖 AI Summary
Current LLM agents lack reproducible, intervenable, and verifiable debugging mechanisms when encountering tool failures such as timeouts, stale data, or description contamination. This work proposes the first fault reproduction–intervention–verification workflow tailored for the Model Context Protocol (MCP), implemented in an open-source web-based workbench. The platform supports real tool-call recording, injection of 12 failure types, cache-matched replay, and real-time retry capabilities. By integrating deterministic rules with an LLM-based adjudicator, the framework enables controllable behavior reproduction and rigorous performance validation. In experiments across five agents and 120 scenarios, the strongest agent completed 105 tasks; notably, the retry mechanism boosted success rates from 30% to 100% for timeout errors, while handling stale data remains challenging.
📝 Abstract
Tool-using LLM agents are mostly evaluated assuming all tools work. When a tool times out, returns a week-stale value, or has its description poisoned in deployment, the developer needs a controlled way to reproduce the failure, test a fix, and confirm the fix worked before deployment. We present AgentCheck, an open-source web workbench that turns an MCP server into an intervention surface. AgentCheck runs an agent against its real tools and records every tool response, then re-runs the agent with the response perturbed by a fault (12 types) injector. Matching tool calls are replayed from cache, and later tool calls go live after the agent diverges. This yields a reproduce-intervene-confirm loop: the developer toggles a mitigation, re-runs against the identical fault, and sees if the failure goes away. Scoring has two parts: deterministic pass/fail rules, plus an LLM judge for interpretive labels, validated against human annotations. Across five agents, the best passes 105/120 scenarios and the weakest only 77. The failures are usually silent, confident use of incorrect tool outputs rather than crashes. On the weakest agent, a retry mitigation raises success on timeout error faults from as few as 30% of cases to 100%, whereas stale-data faults remain near 3-4 of 10 regardless of the mitigation. AgentCheck makes these failure modes reproducible, comparable, and verifiable before deployment.