MCP vs A2A: Comparing AI Agent Protocols for 2025
Vishal Kumar Sharma • June 26th, 2025 • 3 min read • 👁️ 174 views • 💬 0 comments
Interoperability is the key to unlocking true AI collaboration.
AI is evolving beyond standalone chatbots, creating ecosystems of intelligent agents that collaborate. Two key open protocols enabling this are:
- Model Context Protocol (MCP) by Anthropic
- Agent-to-Agent (A2A), spearheaded by Google (and now the Linux Foundation)
Let’s explore what each does, compare their advantages/disadvantages, and understand where they fit.
What Is MCP?
MCP is an open protocol designed to standardize how AI applications connect to data sources and tools, think of it as the “USB‑C for AI” . Launched in November 2024 by Anthropic, it defines a client‑server JSON‑RPC interface that allows LLMs to access files, databases, functions, and more.
- Adoption: OpenAI, Google DeepMind, Microsoft Copilot support it.
- Use case: Tool interoperability e.g., connecting agents to GitHub, CRM, SQL databases
What Is A2A?
A2A is an open protocol enabling agent-to-agent communication. Developed by Google and donated to the Linux Foundation in June 2025, it defines how agents discover, negotiate capabilities, exchange context, and coordinate actions securely.
- Principles: Agent collaboration, vendor-neutral, secure, no shared memory/tools.
- Adoption: Backed by AWS, Cisco, Microsoft, Salesforce, SAP, over 100 organizations in Agent2Agent Foundation.
Advantage & Disadvantage Table
| Feature | MCP | A2A |
|---|---|---|
| Primary Focus | Tool/data access | Agent-to-agent communication |
| Architecture | Client-server JSON-RPC | Peer-to-peer, JSON-RPC/HTTP/SSE |
| Ecosystem | Tools/plugins (GitHub, Slack, DBs) | Agent networking, task delegation |
| Use Cases | Agent/tool workflows | Multi-agent coordination |
| Security Considerations | Requires safe server implementations; risk of tool misuse | Built with secure negotiation, no shared state |
| Complexity | Easier to implement for tool access | Complex interactions between agents |
| Adoption Level | Supported by MCP‑enabled agents (OpenAI, Microsoft) | Backed by enterprise/cloud providers |
| Maturity | Emerging, rapid adoption | Standardizing via Linux Foundation |
When to Use Which
- Use MCP when you need an LLM-based agent to access files, APIs, databases, or tools, like coding assistants or chatbots integrated with CRMs.
- Use A2A when designing systems where multiple intelligent agents must coordinate, delegate, and collaborate, such as enterprise workflow automation or federated AI systems.
Practical Workflow Example
Consider a scenario: Agent A analyzes customer emails; Agent B updates the CRM
- A uses MCP to read and parse incoming email content from Gmail API.
- A uses A2A to request B to log a follow-up task in CRM.
- A2A handles secure negotiation and message transfer.
- B may use MCP to actually execute the CRM update.
This hybrid workflow shows how MCP and A2A complement each other.
Final Thoughts
Both MCP and A2A mark major milestones in the AI agent era:
- MCP solves cross-tool connectivity
- A2A enables agent interoperability and modular AI
Together, they pave the way for collaborative, multi-agent applications.
With industry backing (OpenAI, Microsoft, AWS) and open governance, these protocols are driving toward a standardized agentic web.
References
- MCP introduction & concept
- A2A announcement & Linux Foundation adoption
- MCP adoption by OpenAI, Google, Microsoft
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