Three protocols are shaping how AI systems communicate in 2026: Anthropic’s MCP, Google’s A2A, and the community-driven ACP. They’re not competitors — they solve different problems at different layers.
The one-sentence difference
MCP connects AI to tools and data (vertical). A2A connects AI agents to each other (horizontal). ACP is a community alternative to A2A.
Comparison
| MCP | A2A | ACP | |
|---|---|---|---|
| Purpose | AI ↔ Tools/Data | Agent ↔ Agent | Agent ↔ Agent |
| Creator | Anthropic | Community | |
| Direction | Vertical | Horizontal | Horizontal |
| Maturity | Production-ready | Growing | Early |
| Adoption | Claude, GPT, Cursor, VS Code | Salesforce, SAP, ServiceNow | Emerging |
| Transport | stdio, SSE | HTTP, gRPC | HTTP |
| Governance | Linux Foundation | Linux Foundation | Linux Foundation |
When to use MCP
Use MCP when your AI needs to interact with external systems:
- Call APIs, query databases, read files
- Execute shell commands, send messages
- Any tool integration
This is what most developers need first. See our MCP Complete Guide and build tutorials.
When to use A2A
Use A2A when multiple AI agents need to collaborate:
- Support agent delegates to billing agent
- Research agent sends findings to writing agent
- Agent Swarm coordination across vendors
When to use both
Most production systems combine them:
User → Agent A (uses MCP to read database)
↓ A2A
Agent B (uses MCP to send email)Each agent uses MCP for its tools. A2A handles the agent-to-agent coordination.
For AI coding tool developers
If you’re building tools like Aider, OpenCode, or Continue.dev:
- Start with MCP — it connects your tool to databases, APIs, and services
- Add A2A later — when you need multi-agent workflows
The GDPR angle
MCP servers can run locally (self-hosted), keeping data in your infrastructure. A2A between agents may involve cross-boundary data transfers — check your compliance requirements.
Related: What is MCP? · MCP Complete Guide · MCP Security Risks · AI and GDPR · Future Of Ai Protocols