What Is The Difference Between A2A And MCP? [With Videos]

If you're building AI products, you've likely asked the question: how do you connect different AI systems together? How do you let your AI agents access your company's data, use tools, and interact with other AI agents without rebuilding everything from scratch?

Multi-agent systems and the open agentic web are undoubtedly the way forward. And there’s a lot of work going on to establish agentic protocols to pave the way.

Currently, the two most-talked-about protocols are Google's Agent2Agent (A2A) protocol and Anthropic's Model Context Protocol (MCP). While both aim to connect AI systems, they tackle fundamentally different challenges. Very simply put, A2A is the telephone system for AI agents, while MCP is the universal adapter for AI models and data sources.

Let’s dig in a bit deeper…

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What is the A2A protocol?

Google’s Agent2Agent (A2A) protocol addresses a critical challenge: enabling AI agents, built on diverse frameworks by different companies running on separate servers, to communicate and collaborate effectively—as agents, not just as tools.

Google announced A2A to solve agent interoperability. The protocol lets AI agents from different companies and frameworks work together on complex tasks. Instead of building custom integrations for every AI service, A2A provides a standardized way for agents to find each other, negotiate capabilities, and coordinate work.

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How does A2A work?

A2A operates like a sophisticated directory service combined with a task delegation system. Here's the basic flow:

• Agent discovery: Agents register their capabilities in a standardized format, e.g. an AI Agent Card
• Capability negotiation: When an agent needs help, it queries the network for agents with specific skills
• Task delegation: The requesting agent sends structured task requests to capable agents
• Secure communication: Agents exchange information through encrypted channels
• Result coordination: Multiple agents can collaborate on complex, multi-step tasks

The protocol focuses on autonomous agent-to-agent communication. Your customer service AI could automatically coordinate with your inventory management AI and shipping AI to resolve a complex customer issue—without human intervention or custom code bridges.

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What is MCP?

Anthropic’s Model Context Protocol (MCP) is an open standard that enables developers to build secure, two-way connections between their data sources and AI-powered tools. It aims to standardize how AI models access external data and tools.

Think of MCP like a USB-C port for AI applications. Just as USB-C provides a standardized way to connect your devices to various peripherals and accessories, MCP provides a standardized way to connect AI models to your databases, APIs, file systems, and business applications.

MCP eliminates the need to build custom connectors for every data source your AI needs to access. Instead of writing bespoke integration code, you implement MCP servers that expose your data through a standardized interface.

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How does MCP work?

MCP uses a client-server architecture where your AI application (the client) connects to data sources through MCP servers:

• Server implementation: You create MCP servers that expose your data, tools, or prompts through standardized endpoints
• Client connection: Your AI application connects to MCP servers using the protocol specification
• Resource access: The AI can request specific data, execute tools, or use predefined prompts through the MCP interface
• Bidirectional communication: Both the AI and data sources can initiate communications as needed
• Context integration: The AI receives structured context that improves response quality and accuracy

The protocol supports three main types of integrations: resources (data you want to include in context), tools (functions the AI can execute), and prompts (templates for common tasks).

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What is the difference between Google A2A and Anthropic MCP?

The key difference lies in what they connect and how they operate:

A2A connects AI agents to other AI agents. It's designed for agent-to-agent communication, where autonomous AI systems coordinate with each other to accomplish complex tasks. Think of it as building a network of specialists who can call on each other's expertise.

MCP connects AI models to data and tools. It's designed for model-to-system integration where your AI needs access to databases, APIs, file systems, or business applications. Think of it as giving your AI the ability to read from and interact with all your existing systems.

Scope and purpose:

• A2A: Multi-agent orchestration and collaboration
• MCP: Data integration and tool access

Communication pattern:

• A2A: Peer-to-peer agent communication
• MCP: Client-server data access

Use cases:

• A2A: Complex workflows requiring multiple specialized agents
• MCP: AI applications needing access to enterprise data and systems

Implementation complexity:

• A2A: Requires building or integrating agent frameworks
• MCP: Requires implementing data servers or using existing connectors

Both protocols can complement each other in sophisticated AI architectures. You might use MCP to connect your agents to data sources then use A2A to coordinate multiple data-enhanced agents on complex tasks.

Both of these protocols are crucial to achieving a future where AI agents can work with each other as well as external tools and data sources. These protocols are essentially the accessibility standards for AI agents.

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