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MCP-Persona: Benchmarking LLM Agents on Real-World Personal Applications via Environment Simulation
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Abstract
MCP-Persona benchmark evaluates agent performance on personalized tools interacting with individual accounts and local databases, revealing significant challenges in current SOTA agents.
AI-generated summary
The Model Context Protocol (MCP) has emerged as a transformative standard for connecting large language models (LLMs) with external data sources and tools, and has been rapidly adopted across personal applications and development platforms. However, existing benchmarks predominantly focus on generic information-seeking tools and fail to capture the practical challenges posed by personal social applications, where tools interact with individual accounts or local databases. To bridge this critical gap, we introduce MCP-Persona, the first benchmark specifically designed for evaluating agent performance on real-world, personalized MCP tools. MCP-Persona encompasses a diverse set of widely-used applications, ranging from social media platforms like Reddit and Xiaohongshu (Rednote) to enterprise collaboration suites such as Lark (Feishu) and Slack. Our extensive experiments on various state-of-the-art (SOTA) agents demonstrate their significant struggles with personalized tool use, thereby highlighting the benchmark's crucial role in identifying and addressing these limitations. MCP-Persona is publicly available at https://github.com/wwh0411/MCP-Persona}{https://github.com/wwh0411/MCP-Persona.
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Cite arxiv.org/abs/2606.02470 in a model README.md to link it from this page.
Cite arxiv.org/abs/2606.02470 in a dataset README.md to link it from this page.
Cite arxiv.org/abs/2606.02470 in a Space README.md to link it from this page.
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