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Network and Systems Performance Characterization of MCP-Enabled LLM Agents

arXiv — cs.CL•Wednesday, November 12, 2025 at 5:00:00 AM
NeutralArtificial Intelligence
The recent paper on 'Network and Systems Performance Characterization of MCP-Enabled LLM Agents' highlights the growing importance of the Model Context Protocol (MCP) in the AI community. MCP facilitates enhanced interactions between large language models (LLMs) and external tools, but this comes at a cost. The inclusion of extensive contextual information significantly inflates token usage, which can escalate both monetary costs and computational load on LLM services. The paper presents a detailed measurement-based analysis of these MCP-enabled interactions, revealing critical trade-offs between capability, performance, and cost. By examining various LLM models and MCP configurations, the authors identify key performance metrics such as token efficiency and task success rates. They also propose potential optimizations, including enabling parallel tool calls and implementing robust task abort mechanisms. These insights are crucial for developing more efficient LLM applications, address…
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