Skip to main content
Glama

MCP vs A2A vs ACP: Comparing Agent Protocols

Written by on .

AI-agent
mcp
A2A
ACP
  1. Comparing Agent Protocols: MCP vs A2A vs ACP
    1. Two Protocol Families: Context vs Inter-Agent
      1. MCP – Lightweight, Practical, Widely Adopted
        1. A2A – Asynchronous, Agent-Centric
          1. ACP – Packaging and Remote Execution
            1. Summary Comparison
              1. Key Takeaways
                1. Acknowledgements
                  1. References

                    Comparing Agent Protocols: MCP vs A2A vs ACP

                    As agent frameworks proliferate, developers are starting to ask important architectural questions: How should agents communicate? What protocols support those interactions? And where does MCP fit into this landscape?1

                    This article provides a concise breakdown of the key agent communication protocols and what developers should consider when building AI-driven applications.

                    Two Protocol Families: Context vs Inter-Agent

                    Protocols in this space fall into two broad categories:

                    1. Context-Oriented Protocols (e.g. MCP): Focused on giving models structured access to tools, APIs, and memory.
                    2. Inter-Agent Protocols (e.g. A2A, ACP): Designed to enable autonomous agents to discover, authenticate, and collaborate with other agents over the network.

                    MCP – Lightweight, Practical, Widely Adopted

                    MCP (Model Context Protocol) is designed for simplicity. It allows language models to query a server for tools or structured memory and receive a valid response.1

                    Why MCP matters:

                    • Small and composable
                    • Synchronous and fast by design
                    • Broad adoption across frameworks
                    • Easily extensible for real-world use cases

                    Tool call example:

                    { "tool_name": "getWeather", "args": { "location": "New York" } }

                    Tool registry definition:

                    { "tools": [ { "name": "getWeather", "description": "Fetches weather based on location", "parameters": { "location": "string" } } ] }

                    A2A – Asynchronous, Agent-Centric

                    A2A (Agent-to-Agent) is designed for agents that:

                    • Discover each other
                    • Communicate asynchronously
                    • Negotiate and collaborate over time1

                    Notable traits:

                    • Embraces multi-turn, long-running interactions
                    • Uses status polling and callbacks
                    • Supports decentralized identity and verification

                    Async agent call example (pseudo-structured):

                    { "agent_call": { "target": "agent://weather-bot", "task": "forecast", "parameters": { "city": "Paris" }, "callback_url": "https://myagent.ai/status" } }

                    ACP – Packaging and Remote Execution

                    ACP supports definitions for portable agents that can be downloaded and run in sandboxed environments.1

                    Purpose:

                    • Package agents with metadata
                    • Enable registry-based discovery
                    • Allow self-hosted or distributed execution

                    Agent manifest snippet:

                    { "agent": { "name": "summarizer", "entrypoint": "main.py", "description": "Summarizes news articles", "permissions": ["read", "write"] } }

                    Summary Comparison

                    FeatureMCPA2AACP
                    PurposeTool useAgent collaborationAgent definition + execution
                    Async supportBasicFullPartial
                    Discovery methodManual / RegistryDID / Web URLRegistry-based
                    Hosting modelExternalExternalSelf-hosted / Downloadable
                    ComplexityLowMedium-HighMedium
                    AdoptionHighEarly stageEarly stage

                    Key Takeaways

                    • Start with MCP for synchronous LLM-to-server workflows.
                    • Explore A2A when building collaborative or distributed multi-agent systems.
                    • Watch ACP for developments in portable and secure agent execution.

                    MCP remains the most widely used today because it solves a narrowly scoped problem effectively—making it an excellent foundation for real-world applications.

                    Acknowledgements

                    This guide is based on Laurie Voss's2 insightful presentation at the MCP Summit – "MCP vs ACP vs A2A: Comparing Agent Protocols"1. His clear articulation of protocol design and the nuances between these frameworks shaped the structure of this comparative overview.

                    Special thanks to the LlamaIndex team and the broader agent protocol community for advancing the vision of secure, composable, and collaborative agent ecosystems.

                    References

                    Footnotes

                    1. MCP Summit Talk by Laurie Voss – "MCP vs ACP vs A2A" ↩ ↩2 ↩3 ↩4 ↩5

                    2. Laurie Voss on LinkedIn ↩

                    Written by Om-Shree-0709 (@Om-Shree-0709)