MCP and A2A Protocols: The New Standards for AI Agent Interoperability

The AI agent ecosystem in 2026 looks like the web in 1993 — everyone is building, but nothing talks to anything else. Every framework has its own tool definition format, its own context-passing mechanism, its own way of exposing capabilities.

Two protocols are emerging to fix this: Model Context Protocol (MCP) from Anthropic and Agent-to-Agent (A2A) from Google. Together, they may define the TCP/IP of the agentic era.

The Interoperability Problem

Today's agent frameworks are silos:

• LangChain agents can't natively call CrewAI agent tools.
• An agent built with AutoGen can't discover capabilities exposed by a Semantic Kernel agent.
• Enterprise tools wrapped for one framework must be re-wrapped for another.

This fragmentation means enterprises building multi-agent systems are locked into a single framework, or worse, building custom integration layers between frameworks.

Model Context Protocol (MCP): Agent-to-Tool

MCP, introduced by Anthropic, standardizes how AI models discover and interact with external tools and data sources.

Core Concepts

┌──────────┐     MCP Protocol     ┌──────────┐
│   LLM    │◀───────────────────▶│   MCP    │
│  (Client)│   JSON-RPC / SSE    │  Server  │
└──────────┘                      └──────────┘
                                       │
                                  ┌────┴────┐
                                  │ Tools   │
                                  │ Resources│
                                  │ Prompts │
                                  └─────────┘

MCP defines three primitives:

1. Tools — Functions the model can invoke. Each tool has a name, description, and JSON Schema for parameters.
2. Resources — Data the model can read. Files, database records, API responses exposed as URIs.
3. Prompts — Pre-built prompt templates that guide specific workflows.

Why MCP Matters

Before MCP: Every tool integration is custom. You write a function, describe it in your framework's format, handle serialization, manage errors — differently for every framework.

After MCP: Write one MCP server. Any MCP-compatible client (Claude, VS Code, Cursor, custom agents) can discover and use your tools automatically.

{
  "name": "query_database",
  "description": "Execute a read-only SQL query against the production database",
  "inputSchema": {
    "type": "object",
    "properties": {
      "query": { "type": "string", "description": "SQL SELECT query" },
      "database": { "type": "string", "enum": ["analytics", "operations"] }
    },
    "required": ["query", "database"]
  }
}

MCP Transport Layers

MCP supports multiple transports:

• stdio — For local tools running as subprocesses.
• HTTP + Server-Sent Events (SSE) — For remote tools over the network.
• Streamable HTTP — The latest transport for efficient bidirectional communication.

Agent-to-Agent (A2A) Protocol: Agent-to-Agent

While MCP connects agents to tools, A2A (from Google) connects agents to other agents.

Core Concepts

┌──────────┐     A2A Protocol     ┌──────────┐
│  Agent A │◀───────────────────▶│  Agent B │
│ (Client) │  Agent Card + Tasks │ (Remote) │
└──────────┘                      └──────────┘

A2A defines:

1. Agent Cards — JSON documents describing an agent's capabilities, skills, and endpoint. Agents publish their cards for discovery.
2. Tasks — Units of work with lifecycle states (submitted, working, input-required, completed, failed).
3. Messages — Communication between agents within a task, containing text, files, or structured data.
4. Artifacts — Output produced by an agent in response to a task.

Agent Discovery via Agent Cards

{
  "name": "Financial Analysis Agent",
  "description": "Analyzes financial statements and produces risk assessments",
  "url": "https://agents.example.com/financial-analysis",
  "skills": [
    {
      "id": "balance-sheet-analysis",
      "name": "Balance Sheet Analysis",
      "description": "Analyzes balance sheets for risk indicators"
    },
    {
      "id": "revenue-forecasting",
      "name": "Revenue Forecasting",
      "description": "Projects revenue based on historical data and market conditions"
    }
  ],
  "authentication": {
    "schemes": ["OAuth2"]
  }
}

An orchestrator agent can discover available agents, read their capability cards, and delegate tasks to the most appropriate agent — all without hard-coded integrations.

Task Lifecycle

submitted → working → completed
                   → failed
                   → input-required → working → completed

The input-required state enables human-in-the-loop patterns: an agent can pause, request clarification from a human or another agent, and resume.

MCP + A2A: Complementary, Not Competing

The protocols solve different problems and work together:

| Aspect | MCP | A2A | |---|---|---| | Primary Use | Agent ↔ Tool | Agent ↔ Agent | | Discovery | Tool capabilities | Agent capabilities | | Communication | Function calls (request/response) | Task delegation (async lifecycle) | | State | Stateless tool calls | Stateful task tracking | | Originator | Anthropic | Google |

In a complete system:

• An agent uses MCP to call tools (databases, APIs, file systems).
• An agent uses A2A to delegate subtasks to other agents.
• The orchestrator uses both to compose complex workflows.

Enterprise Adoption Considerations

Security

Both protocols need robust security for enterprise deployment:

• MCP: Servers must validate that the calling client has authorization for each tool. OAuth 2.1 support is being standardized.
• A2A: Agent cards include authentication requirements. Enterprise deployments need mutual TLS, API key rotation, and audit logging.

Observability

Standardized protocols enable standardized observability. Every MCP tool call and A2A task delegation can be traced, logged, and audited uniformly — regardless of which framework the individual agent uses internally.

Vendor Neutrality

The most significant benefit: these protocols decouple agent capabilities from agent frameworks. An enterprise can build MCP servers for their internal tools and make them available to agents built with any framework. Similarly, A2A-compliant agents from different vendors can collaborate on shared workflows.

The Road Ahead

Both protocols are evolving rapidly. Key developments to watch:

• MCP auth standardization — OAuth 2.1 integration for secure remote tool access.
• A2A streaming — Real-time streaming responses for long-running agent tasks.
• Cross-protocol bridges — Frameworks that natively support both MCP (for tools) and A2A (for agents).
• Enterprise registries — Centralized directories of available MCP servers and A2A agents within an organization.

Our Implementation at ATMA-AI

At ATMA-AI, we build our enterprise tool integrations as MCP servers by default — making them instantly available to any MCP-compatible agent. Our multi-agent orchestration layer is being built with A2A support, enabling our agents to collaborate with agents from other vendors and frameworks.

Building interoperable agent systems? Talk to our platform team.