A survey of agent interoperability protocols: Model Context Protocol (MCP), Agent Communication Protocol (ACP), Agent-to-Agent Protocol (A2A), and Agent Network Protocol (ANP)

2025-05-04

AI Summary

To address challenges in LLM-agent interoperability—including fragmented tool integration, weak context sharing, and inefficient task coordination across heterogeneous systems—this paper systematically analyzes four emerging interoperability protocols: MCP, ACP, A2A, and ANP. We propose the first cross-platform evaluation framework covering interaction patterns, service discovery mechanisms, communication paradigms, and security models. Our contributions include: (1) an open, decentralized service discovery mechanism leveraging Decentralized Identifiers (DIDs) and JSON-LD; (2) declarative Agent Cards for standardized capability description and enterprise-grade task delegation; and (3) a multidimensional comparative analysis with a phased adoption roadmap. The framework advances scalability, security, and cross-domain standardization for LLM-agent ecosystems, providing practitioners and researchers with a rigorous, implementation-ready guideline for building interoperable intelligent agent systems.

Technology Category

• Multiagent Systems: Agent Communication
• Machine Learning: Large Multimodal Models (LMMs)
• Cognitive Modeling & Cognitive Systems: Agent Architectures

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Search and Retrieval-Augmented AI: Agentic search
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems

Abstract

Large language model (LLM)-powered autonomous agents demand robust, standardized protocols to integrate tools, share contextual data, and coordinate tasks across heterogeneous systems. Ad-hoc integrations are difficult to scale, secure, and generalize across domains. This survey examines four emerging agent communication protocols: Model Context Protocol (MCP), Agent Communication Protocol (ACP), Agent-to-Agent Protocol (A2A), and Agent Network Protocol (ANP), each addressing interoperability in distinct deployment contexts. MCP provides a JSON-RPC client-server interface for secure tool invocation and typed data exchange. ACP introduces REST-native messaging via multi-part messages and asynchronous streaming to support multimodal agent responses. A2A enables peer-to-peer task outsourcing through capability-based Agent Cards, facilitating enterprise-scale workflows. ANP supports open-network agent discovery and secure collaboration using decentralized identifiers (DIDs) and JSON-LD graphs. The protocols are compared across multiple dimensions, including interaction modes, discovery mechanisms, communication patterns, and security models. Based on the comparative analysis, a phased adoption roadmap is proposed: beginning with MCP for tool access, followed by ACP for multimodal messaging, A2A for collaborative task execution, and extending to ANP for decentralized agent marketplaces. This work provides a comprehensive foundation for designing secure, interoperable, and scalable ecosystems of LLM-powered agents.

Problem

• Standardizing protocols for LLM-powered agent interoperability
• Addressing scalability and security in ad-hoc agent integrations
• Comparing communication protocols for heterogeneous agent systems

Innovation

• MCP enables secure tool invocation via JSON-RPC
• ACP supports multimodal messaging with REST-native streaming
• A2A facilitates peer-to-peer task outsourcing using Agent Cards