MCP, A2A & ACP

Often MCP (Model Context Protocol) and A2A (Agent2Agent Protocol) protocols confuse AI developers and it’s difficult to choose between them. This confusion can be even greater when the ACP (Agent Communication Protocol) comes into play as well.

MCP is an open-source standard (introduced by Anthropic) for connecting AI applications to external systems through standard REST API calls (agent-to-tool communication), whereas A2A and ACP represent two major technical paradigms for communication within AI multi-agent systems: cross-platform interoperability and local/edge autonomy.

A2A emphasizes robust interconnection across different vendors and supports sophisticated collaborative workflows, making it the leading option for cloud-native and distributed multi-agent deployments. In contrast, ACP focuses on low-latency, local-first operation independent of cloud services, making it ideal for privacy-critical, bandwidth-limited, or edge-computing environments. Each protocol has its own priorities in terms of design philosophy, ecosystem development, and standardization efforts, and both are expected to move toward greater openness over time. It is advised to select the protocol stack that aligns best with specific application requirements and enterprise-wide integration guidance.

Note: ACP has joined forces with the A2A community under the Linux Foundation (https://github.com/orgs/i-am-bee/discussions/5).

Note: Anthropic has donated the MCP standard to a newly formed Linux Foundation group, named Agentic AI Foundation (AAIF). Members of the group are Anthropic (MCP), OpenAI (Agents.md) and Block (Goose framework).

MCP

Technical Characteristics

• MCP defines a standardized way for the automation platform to deliver task instructions, along with relevant context, to downstream agents or digital workers.
• Designed for enriching a single model’s context with tools, memory and resources.

Strengths

• Standardization and Reusability: You can build a tool once and connect it to multiple AI models and agent frameworks, reducing redundant work.
• Dynamic Data Access: It allows Large Language Models (LLMs) to access real-time information, overcoming the limitation of their static, pre-trained knowledge.
• Simplified Development: It makes it easier for developers to build and integrate AI agents.
• Enhanced AI Capabilities: Provides AI with access to a broader ecosystem of tools and data, making them more powerful.
• Future-Proof and Scalable: The protocol is designed to support the evolution of AI towards more complex, interconnected systems.

Limitations

• Operational Overhead: Implementing and managing MCP can be complex, especially for smaller teams or simpler projects.
• Security and Permissions: The current version lacks a granular permissions model, with access control managed at the session level.
• Multi-Tenancy: It doesn’t natively support scenarios where multiple users access a shared MCP server simultaneously.
• Workflow Management: It lacks built-in features for managing complex, multi-step tasks.
• Token Consumption: Using many connected servers can lead to high token consumption, which can impact the efficiency of the AI agent.

A2A

Technical Characteristics

• Developed under Google’s leadership and intended as a universal interoperability protocol for agents across platforms and vendors.
• Utilizes HTTP/HTTPS as the transport layer, with JSONRPC 2.0 as the primary communication format, and supports Server Sent Events (SSE) for streaming interactions.
• Introduces an “Agent Card” metadata model that enables online/offline agent discovery, capability descriptions, and authentication requirement definitions.
• Supports multi-step collaboration, including task delegation, message and artifact exchange, and user experience negotiation.
• Places strong emphasis on security, with support for OAuth2, API key–based authorization, and granular capability scoping.
• Designed to be “Web-native,” making it highly compatible with existing cloud infrastructures, API gateways, and standard enterprise security frameworks.

Strengths

• Excellent cross-platform compatibility, making it ideal for heterogeneous environments and multi-vendor ecosystems.
• Provides robust state management mechanisms, including sessions, tasks, and agent memory.
• Capable of handling streaming data and sophisticated collaborative workflows.
• Offers comprehensive security features that integrate smoothly with enterprise security systems.
• Well-suited for large, distributed, and cloud-native AI deployments.

Limitations

• Strong reliance on network connectivity makes it unsuitable for offline or edge computing scenarios.
• The protocol stack is relatively complex, resulting in a higher initial integration cost.
• Requires developers to understand multiple protocol layers (HTTP + JSON‑RPC), creating a slightly higher learning curve compared to REST.

ACP

Technical Characteristics

• Developed collaboratively by IBM Research and the BeeAI community, with an emphasis on multi‑agent cooperation at the local and edge levels.
• Offers versatile communication options, including RESTful HTTP, gRPC, ZeroMQ, local message buses, and more.
• Prioritizes autonomous local operation and discovery, suitable for cloudless environments, supports offline registration, and allows distribution via Docker images.
• Built on a decentralized, event-driven architecture designed for lightweight and low‑latency communication.
• Enables capability declaration, semantic descriptions, and automatic routing.
• Strong focus on privacy, local control, and minimizing network overhead.

Strengths

• Very low latency, ideal for edge computing, IoT systems, and robotics applications.
• Highly flexible deployment with no reliance on cloud services or external registries.
• Minimal resource usage, making it suitable for embedded or resource‑limited devices.
• Supports multiple communication layers and is easy to extend or customize.
• Community-driven and friendly to open-source development.

Limitations

• Limited interoperability across diverse platforms, as it primarily targets local or same-domain agent interactions.
• Smaller ecosystem compared to A2A, with weaker integration into cloud environments.
• Standardization is still maturing, features like capability discovery and governance remain under development.
• Documentation and tooling are not yet as comprehensive or polished as those available for A2A.

Comparison matrix

The key differences between the three protocols can be summarized in the following table:

References:

https://www.ibm.com/think/topics/agent-communication-protocol

https://community.ibm.com/community/user/blogs/gyanendra-s-rathor/2025/06/26/understanding-mcp-acp-and-a2a

https://modelcontextprotocol.io/docs/getting-started/intro

https://a2a-protocol.org/latest/#how-does-a2a-work-with-mcp