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arXiv · 2601.22633

MCP-Diag: A Deterministic, Protocol-Driven Architecture for AI-Native Network Diagnostics

Devansh Lodha·Mohit Panchal·Sameer G. Kulkarni
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Abstract

The integration of Large Language Models (LLMs) into network operations (AIOps) is hindered by two fundamental challenges: the stochastic grounding problem, where LLMs struggle to reliably parse unstructured, vendor-specific CLI output, and the security gap of granting autonomous agents shell access. This paper introduces MCP-Diag, a hybrid neuro-symbolic architecture built upon the Model Context Protocol (MCP). We propose a deterministic translation layer that converts raw stdout from canonical utilities (dig, ping, traceroute) into rigorous JSON schemas before AI ingestion. We further introduce a mandatory "Elicitation Loop" that enforces Human-in-the-Loop (HITL) authorization at the protocol level. Our preliminary evaluation demonstrates that MCP-Diag achieving 100% entity extraction accuracy with less than 0.9% execution latency overhead and 3.7x increase in context token usage.

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Devansh Lodha, Mohit Panchal, Sameer G. Kulkarni. 2026-01-30. MCP-Diag: A Deterministic, Protocol-Driven Architecture for AI-Native Network Diagnostics. https://arxiv.org/abs/2601.22633

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