MCP vs A2A: Which Protocol Does Your AI Agent Need?
A deep technical comparison of Model Context Protocol and Agent-to-Agent Protocol — when to use each, how they complement each other, and real architectures.
Understanding the Protocols
MCP (Model Context Protocol) standardizes how AI agents discover and use external tools. It's the 'USB-C for AI' — a universal interface for capability access.
A2A (Agent-to-Agent Protocol) standardizes how AI agents communicate with each other. It enables negotiation, task delegation, and commerce between autonomous agents.
Key Differences
| Aspect | MCP | A2A |
|---|---|---|
| Purpose | Agent → Tool | Agent → Agent |
| Communication | Request/Response | Negotiation/Delegation |
| Discovery | Tool catalog | Agent directory |
| Payment | Per-invocation | Per-task |
| Autonomy | Tool is passive | Both agents are active |
When to Use MCP
Use MCP when your agent needs to use a capability — send an email, process an image, query a database. The tool doesn't make decisions; it executes a specific function.
When to Use A2A
Use A2A when your agent needs to collaborate with another autonomous agent — negotiate a price, delegate a complex task, coordinate a multi-step workflow. Both agents make decisions.
Complementary Architecture
Most production systems use both protocols:
- Agent receives a complex task via A2A
- Agent decomposes the task into subtasks
- Agent uses MCP to find and invoke specific tools
- Agent delivers results back via A2A
This is the architecture SkillExchange is built for — MCP for skill discovery, A2A for agent collaboration.
Real-World Example
A customer support agent receives a complaint (via A2A from the sales agent). It uses MCP to: look up the order, process a refund, send an apology email, and update the CRM. It reports back to the sales agent (via A2A) with the resolution.
Conclusion
MCP and A2A aren't competing — they're complementary. Use MCP for tool access, A2A for agent collaboration. Together, they form the foundation of the autonomous AI economy.