A2A (Agent-to-Agent) vs. MCP (Model Context Protocol)

Here’s a comparison between A2A (Agent-to-Agent Protocol) and MCP (Model Context Protocol) in the context of AI agents:

A2A (Agent-to-Agent Protocol):

• Primary Focus: Standardizing communication and interoperability between different AI agents, regardless of their origin or framework. Aims to give AI agents a common language for collaboration.
• Goal: To enable seamless collaboration between autonomous agents to solve complex tasks.
• Analogy: Like a project manager coordinating a team of engineers.
• Key Features:
  • Capability Discovery: Agents advertise their skills using an “Agent Card”.
  • Task-Oriented Communication: Agents exchange “Tasks” to get work done.
  • Rich Data Exchange: Supports various data formats (JSON, files, etc.) as “Parts” within messages.
  • Flexible Interaction Patterns: Supports request/response, streaming (SSE), and push notifications.
  • Security: Includes mechanisms for declaring authentication requirements.
• Developed by: Google, with support from over 50 industry partners.
• Communication Mechanism: Primarily uses JSON-RPC 2.0 over HTTP(S), with support for Server-Sent Events (SSE).
• Emphasis: High-level dialogue, ongoing tasks with state tracking, flexibility, judgment, and delegation between agents.
• A2A Protocol Specification
• Learn More About A2A

MCP (Model Context Protocol):

• Primary Focus: Standardizing how AI models (often LLMs) connect with and utilize external data sources and tools at runtime. Aims to provide AI agents with the necessary context and tools.
• Goal: To provide AI agents with the necessary context and tools to perform specific tasks effectively.
• Analogy: Like an engineer using precise tools to perform a specific job.
• Key Features:
  • Structured Schemas: Uses strict schemas (often JSON Schema) to define tool interactions.
  • Single-Stage Execution: Typically focuses on executing a specific action with a tool.
  • Low-Level, Instruction-Based: Capabilities are described with very specific instructions.
  • Client-Server Architecture: AI applications (clients) connect to MCP servers exposing tools/data.
  • Supports various transports: Stdio (local), HTTP + SSE (networked).
• Developed by: Anthropic, with growing support (including Google).
• Communication Mechanism: Can use Stdio or HTTP with Server-Sent Events (SSE), based on JSON-RPC 2.0.
• Emphasis: Structured actions, precise execution of commands, integrating external knowledge and capabilities into the model’s context.
• MCP Specification
• MCP Documentation by Anthropic

Key Differences:

Feature	A2A (Agent-to-Agent Protocol)	MCP (Model Context Protocol)
Main Objective	Agent-to-agent communication and coordination	Agent-to-tool/external context communication
Focus	How agents talk to each other	How agents access and use external resources
Communication Style	Natural language, conversational	Structured commands and data exchange
Task Management	Ongoing, multi-stage, stateful	Single-stage execution, often stateless
Abstraction Level	Higher-level, agentic capabilities	Lower-level, specific tool/data interaction
Analogy	Project Manager	Engineer with specific tools
Developed by	Google	Anthropic
Relationship	Complementary	Complementary

How They Complement Each Other:

Google states that A2A is designed to complement MCP, not compete. They can work together in scenarios where:

• MCP for Tools, A2A for Coordination: Individual agents use MCP to interact with specific tools (e.g., a database or a calculator), while A2A facilitates communication and coordination between these agents to achieve a larger task.
• Building Complex Systems: An AI system might use MCP to gather necessary data from external sources and then utilize A2A to coordinate with other agents to process that information or take subsequent actions.

In essence:

• MCP equips individual AI agents with the ability to use tools and access external information.
• A2A enables these individual agents to work together as a team to achieve more complex goals.

Both protocols are important for the development of sophisticated and practical multi-agent AI systems. The choice of which to use, or whether to use both, depends on the specific architecture and requirements of the AI application being built.

Further Resources:

• Google’s A2A Protocol: Here’s What You Need to Know (LearnOpenCV)
• MCP vs A2A: Which Protocol Is Better For AI Agents? (Blott Studio)
• A2A and MCP: Start of the AI Agent Protocol Wars? (Koyeb)
• Google’s Agent-to-Agent (A2A) and Anthropic’s Model Context Protocol (MCP) (Gravitee.io)
• MCP vs A2A: Everything you need to know (Composio)