MCP and A2A get mentioned together, sometimes interchangeably. They’re not the same thing, and confusing them will lead you to solve the wrong problem.
MCP connects AI to your tools and data. When your AI assistant needs to query your CRM, read documents, or check inventory levels, that’s MCP (Model Context Protocol). It’s how an AI system reaches out to use external resources: databases, APIs, file systems.
A2A enables AI systems to coordinate with each other. When your customer service AI needs to work with your logistics AI to solve a complex fulfillment problem, that’s A2A (Agent-to-Agent). It’s how multiple AI systems collaborate on tasks that require different types of expertise.
When You Need MCP
MCP is relevant when you’re connecting a single AI system to external capabilities. Your AI assistant needs to:
- Query your CRM for customer history
- Read and analyze documents from SharePoint
- Check real-time inventory levels
- Execute actions in your business systems
This is AI-to-tool communication. The AI is using resources, not collaborating with peers.
When You Need A2A
A2A becomes relevant when you have multiple specialized AI systems that need to work together.
Complex problem resolution: Your IT helpdesk AI encounters a network issue it can’t resolve alone. Instead of escalating to a human, it coordinates with your network monitoring AI to gather diagnostics, consults with your vendor management AI to check service contracts, and works with your scheduling AI to coordinate maintenance.
Multi-stage processing: A contract arrives for review. Your document AI extracts key terms, identifies it needs legal review, and coordinates with your legal AI for analysis, your compliance AI for regulatory checks, and your workflow AI for routing approvals.
Dynamic resource allocation: Higher-than-expected demand shows up in your analytics. Your inventory AI already knows stock is tight. It pulls logistics into the conversation to redistribute from overstocked regions, then brings procurement online to expedite new supplies. Three systems, one coordinated response.
These scenarios require AI systems collaborating like a team of specialists, with delegation, coordination, and shared problem-solving. That’s fundamentally different from a single AI using tools.
The Practical Test
Ask yourself: Is my AI using something, or working with something?
If your customer service AI needs to look up order status, that’s MCP. It’s using your order management system as a tool.
If your customer service AI needs to collaborate with your logistics AI to create a custom delivery solution that neither could handle alone, that’s A2A. Two systems working together on a multi-step problem.
What This Means for Vendor Conversations
When evaluating AI platforms, these are different questions:
For MCP: “What systems can your AI connect to? What data sources can it access? What actions can it take?”
For A2A: “Can your AI delegate work to other AI systems? Can it receive delegated tasks? What protocols do you support for agent communication?”
Most organizations today need MCP. You have an AI system and you want it to access your data and tools.
A2A becomes relevant as you accumulate multiple AI systems and start thinking about orchestration: getting them to work together rather than operating as isolated tools. The protocol was developed by Google and is now managed by the Linux Foundation with broad vendor support, so it’s worth tracking even if you’re not ready to implement it.
Conflating them leads to asking the wrong questions and buying the wrong solutions.