A Comparative Study of MCP and A2A for Inter-Agent Coordination in LLM-Based Systems

Abstract. Recent industry practice has seen the rapid emergence of agentic systems composed of heterogeneous, tool- and LLM-mediated agent components, raising practical questions about inter-agent coordination and protocol design. This paper presents an implementationgrounded comparison of the Model Context Protocol (MCP) and the Agent2Agent (A2A) protocol, from a multi-agent systems engineering perspective, using an inter-agent coordination scenario involving LLMbased agents. We evaluate an MCP-based and an A2A-based multi-agent implementation of the same software engineering task against a set of requirements derived from prior literature and discussions with industry partners, including agent discoverability, multi-part messaging, multiturn conversations, asynchronous communication, observability, interoperability, and access control. The results evidence that MCP can support inter-agent coordination in constrained LLM-based systems through a comparatively lightweight implementation model with lower coordination complexity, although coordination concerns such as conversational state management and task lifecycle handling must be implemented explicitly at the application layer.

Introduction. Recent advances in large language models (LLMs) have led to the emergence of contemporary agentic systems in which multiple, specialised agent components collaborate to accomplish complex tasks. In the classical Multi-Agent Systems (MAS) literature, Wooldridge defines agents under the weak notion of agency as autonomous computational entities that are situated in an environment, perceive and act upon that environment, exhibit reactive and proactive behaviour in pursuit of their objectives, and are capable of interacting with other agents through explicit communication mechanisms [48]. This weak notion contrasts with stronger notions of agency, which assume rich internal mental states [40] or normative social semantics [41]. Nevertheless, in contemporary LLM-based practice, agents are often defined more pragmatically as LLM-enabled components that participate in multi-step task execution within a software system.

Discussion / Conclusion. The results indicate that the MCP-based implementation supports agent discoverability, multi-part messaging, asynchronous and streaming communication, interoperability, and access control, while it does not provide native support for multi-turn conversations and only partially supports agent observability. In comparison with the A2A-based implementation, MCP satisfies the requirements in largely the same way, with the notable exception of multi-turn conversations, which are explicitly supported by A2A but not natively supported by MCP, while observability remains only partially supported in both. The key distinction with regard to these requirements concerns multi-turn, stateful task handling: A2A provides explicit protocol-level support for long-running tasks, task states, and input-required interactions, whereas MCP requires these aspects to be realised through explicit orchestration logic implemented in the application layer.