The increasing complexity and dynamism of urban environments necessitate advanced tools for comprehensive and timely diagnostics. Traditional methods are often labor-intensive, fragmented, and struggle to synthesize the vast, heterogeneous data streams generated by modern cities. This paper presents a novel theoretical framework for a multi-agent system that synergistically integrates Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG) to deliver automated and customizable urban diagnostics. The proposed system employs a modular, plug-and-play architecture orchestrated by a core LLM, which coordinates a team of specialized agents for tasks including data extraction, analysis, auto-debugging, and report generation. A key innovation is the use of a handbook driven RAG mechanism, where structured technical guides for various data sources and thematic domains serve as a verifiable knowledge base, grounding the system's outputs in factual, domain-specific information. This knowledge-driven approach enables the dynamic generation of code, the handling of diverse data formats, and the assembly of complex diagnostic reports tailored to user specifications provided in natural language. By outlining the system's architecture, workflow, knowledge management strategy, and core theoretical principles, this paper establishes a foundational contribution towards developing more intelligent, adaptive, and reliable systems for urban planning and governance.