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

• URL: http://arxiv.org/abs/2601.22633v1
• Date: Fri, 30 Jan 2026 06:49:25 GMT
• Title: MCP-Diag: A Deterministic, Protocol-Driven Architecture for AI-Native Network Diagnostics
• Authors: Devansh Lodha, Mohit Panchal, Sameer G. Kulkarni,
• Abstract summary: This paper introduces MCP-Diag, a hybrid neuro-symbolic architecture built upon the Model Context Protocol (MCP)<n>We propose a deterministic translation layer that converts rawout from canonical utilities (dig, ping, traceroute) into rigorous schemas before AI ingestion.<n>We also introduce a mandatory "Elicitation Loop" that enforces Human-in-the-Loop (HITL) authorization at the protocol level.
• Score: 0.08921166277011344
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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