NetMCP: Network-Aware Model Context Protocol Platform for LLM Capability Extension

2025-10-15

AI Summary

Current MCP systems rely solely on semantic matching, rendering them vulnerable to network latency fluctuations and server failures, thereby compromising the robustness of LLM-based tool invocation. To address this, we propose NetMCP—the first network-aware MCP experimental platform—that jointly optimizes semantic matching and network quality through real-time monitoring of network QoS and server health. Its core innovation is the SONAR routing algorithm, which unifies semantic similarity, latency prediction, and service availability modeling into a single dynamic routing decision framework—a novel integration in MCP design. NetMCP supports heterogeneous service integration and latency-sequence-driven stress testing. Experimental results demonstrate that, compared to semantic-only and LLM-based baselines, SONAR improves task success rate by 23.6%, reduces average completion time by 31.4%, and decreases failure count by 47.2%.

Technology Category

• Planning, Routing, and Scheduling: Planning with Language Models
• Search and Optimization: Learning to Search
• Multiagent Systems: Agent Communication

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Web performance, measurement, and characterization
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics

Abstract

Large Language Models (LLMs) remain static in functionality after training, and extending their capabilities requires integration with external data, computation, and services. The Model Context Protocol (MCP) has emerged as a standard interface for such extensions, but current implementations rely solely on semantic matching between users' requests and server function descriptions, which makes current deployments and simulation testbeds fragile under latency fluctuations or server failures. We address this gap by enhancing MCP tool routing algorithms with real-time awareness of network and server status. To provide a controlled test environment for development and evaluation, we construct a heterogeneous experimental platform, namely Network-aware MCP (NetMCP), which offers five representative network states and build a benchmark for latency sequence generation and MCP server datasets. On top of NetMCP platform, we analyze latency sequences and propose a Semantic-Oriented and Network-Aware Routing (SONAR) algorithm, which jointly optimizes semantic similarity and network Quality of Service (QoS) metrics for adaptive tool routing. Results show that SONAR consistently improves task success rate and reduces completion time and failure number compared with semantic-only, LLM-based baselines, demonstrating the value of network-aware design for production-scale LLM systems. The code for NetMCP is available at https://github.com/NICE-HKU/NetMCP.

Problem

• Enhancing LLM capability extension through network-aware tool routing
• Addressing fragility of semantic-only MCP implementations under network fluctuations
• Optimizing tool selection with real-time network and server status

Innovation

• Enhanced MCP routing with real-time network awareness
• Proposed SONAR algorithm optimizing semantic and QoS metrics
• Built NetMCP platform with five network states for testing