MCP-Diag: A Deterministic, Protocol-Driven Architecture for AI-Native Network Diagnostics
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arXiv
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| Autores principales: | , , |
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| Formato: | Preprint |
| Publicado: |
2026
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| _version_ | 1866912862179426304 |
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| author | Lodha, Devansh Panchal, Mohit Kulkarni, Sameer G. |
| author_facet | Lodha, Devansh Panchal, Mohit Kulkarni, Sameer G. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_22633 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | MCP-Diag: A Deterministic, Protocol-Driven Architecture for AI-Native Network Diagnostics Lodha, Devansh Panchal, Mohit Kulkarni, Sameer G. Networking and Internet Architecture Artificial Intelligence 68M10 C.2.3; I.2.7 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. |
| title | MCP-Diag: A Deterministic, Protocol-Driven Architecture for AI-Native Network Diagnostics |
| topic | Networking and Internet Architecture Artificial Intelligence 68M10 C.2.3; I.2.7 |
| url | https://arxiv.org/abs/2601.22633 |