Recent benchmarks reveal that even top-tier AI models like Google’s Gemini 5 and OpenAI’s GPT-5 face significant challenges using Model Context Protocol (MCP), an emerging middleware designed to enhance generative AI. Despite their potential to connect with various resources, these models struggle with multi-step processes and complex queries, often requiring excessive interactions that lead to delays. Studies from institutions like Accenture and UC Berkeley indicate performance declines as tasks shift from single-server to multi-server scenarios, challenging the models’ ability to engage in effective long-horizon planning. The research suggests a need for targeted training to adapt AI models for MCP usage. Additionally, a new dataset, Toucan, shows promise in improving performance for smaller models, although the efficacy of these models in specific private environments remains uncertain. To advance AI capabilities, training in MCP interactions is crucial, highlighting the need for continuous evolution and refinement in AI technologies.
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