Architectural Comparison of LLM Tool Integration Paradigms

This comparison includes the most representative paradigms for integrating external tools into Large Language Model (LLM) agents, based on a systematic review of recent literature including "Bridging AI and Software Security: A Comparative Vulnerability Assessment of LLM Agent Deployment Paradigms" (arXiv:2507.06323), "Model Context Protocol: Landscape, Security Threats, and Future Research Directions" (arXiv:2503.23278), and "A Survey of Agent Interoperability Protocols: MCP, ACP, A2A, ANP" (arXiv:2505.02279). The comparison covers six core architectural properties. Integration Mechanism, Coupling Level, Communication Protocol, Tool Discovery, Interoperability, and Scalability evaluated across six paradigms: Function Calling (FC), Model Context Protocol (MCP), Legacy API Wiring, ACP, A2A, and ANP.
Integration Mechanism refers to the structural pattern by which an LLM agent connects to and orchestrates external tools, ranging from centralized orchestration in Function Calling to distributed client–server separation in MCP and peer-to-peer delegation in A2A and ANP. Coupling Level captures the degree of interdependence between the agent's reasoning layer and its tool execution environment, spanning tight coupling in legacy approaches to fully decentralized designs. Communication Protocol characterizes the transport and encoding standards governing agent–tool exchanges, including proprietary JSON APIs, standardized JSON-RPC 2.0, RESTful HTTP, Server-Sent Events (SSE), and decentralized DID+JSON-LD channels. Tool Discovery describes how agents locate and register available tools at runtime, contrasting static prompt-embedded schemas with dynamic server-side discovery and capability-based advertisement via Agent Cards. Interoperability reflects the degree to which a paradigm supports cross-platform, cross-vendor, or open-internet integration, from proprietary platform-specific implementations to open-standard vendor-neutral protocols. Scalability characterizes the capacity of each paradigm to sustain performance and maintainability as the number of tools, agents, and deployment environments grows, from fragile manually wired systems to lightweight runtime-independent and distributed multi-server architectures.