A new benchmark called MCPAgentBench aims to answer a blunt question: can AI agents actually tell the right tool from a pile of similar-looking ones?
Researchers built the benchmark from real-world Model Context Protocol (MCP) definitions, pairing authentic tasks with simulated tools rather than live external services other benchmarks depend on. Agents are dropped into a dynamic sandbox and handed a list of candidate tools that includes deliberate distractors, forcing them to select and discriminate rather than just execute a single obvious call. The team also built metrics that track not just whether a task gets done, but how efficiently the agent gets there. Tests on current state-of-the-art LLMs turned up sizable gaps in handling complex, multi-step tool invocations, and the code is open-source on GitHub.
That distractor design is the useful part. MCP has become the default way to wire LLMs into external tools since Anthropic introduced it, and most benchmarks so far have measured whether an agent can call a tool at all, not whether it can pick correctly when several plausible options are on the table. That is the failure mode that actually breaks agents in production, where a tool list rarely has just one right answer sitting alone.
Another agent benchmark won't fix tool selection by itself, but this one at least measures the right problem.