This is a detailed comparison of two leading protocols for building and orchestrating AI agents: the Model Context Protocol (MCP) and the Agent-to-Agent (A2A) protocol. As I have already provided comprehensive introductions to MCP in these articles [link1, link2, link3] and to A2A in these articles [link1, link2 , link3], I will now focus on a direct comparison.

A Comprehensive Schema Analysis
I’ve conducted a detailed schema comparison of the Model Context Protocol (MCP) and the Agent-to-Agent (A2A) protocol to highlight key differences in their design and intended functionality. While both aim to facilitate communication involving AI agents, they emphasize distinct features. I will illustrate these differences with a practical example

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Core Design Philosophies
MCP (Model Context Protocol):

Resource-centric architecture built around URI-addressable resources
Emphasizes flexible data access patterns (get, set, subscribe)
Provides granular control with client-side workflow orchestration
Designed for direct interaction with model capabilities
A2A (Agent-to-Agent):

Agent-centric design focused on standardized interoperability
Built around structured task management and agent workflows
Formalizes agent discovery and capability advertisement
Optimized for orchestrating multi-agent systems
Key Similarities
JSON-based Communication Structure Both protocols leverage JSON for structured data exchange, providing a familiar format for developers.
MIME Type Support Both handle diverse content types through standardized MIME specifications, enabling exchange of text, images, audio, and other media.
Asynchronous Operation Support Both protocols incorporate mechanisms for non-blocking operations, though through different approaches.
AI Agent Interaction Both ultimately serve to facilitate communication involving AI systems, whether model-to-client or agent-to-agent.

Key Differences with Examples

  1. Agent Discovery and Capabilities A2A: Employs a dedicated “AgentCard” structure that explicitly defines identity, capabilities, and communication requirements:

{
"name": "DocumentAnalyzerAgent",
"description": "Analyzes documents and extracts key information",
"url": "https://doc-analyzer.example.com",
"capabilities": {
"streaming": true,
"pushNotifications": true
},
"authentication": {
"schemes": ["Bearer"]
},
"defaultInputModes": ["application/pdf", "text/plain"],
"skills": [
{
"id": "extractEntities",
"name": "Extract Entities",
"description": "Identifies and extracts named entities from documents",
"inputModes": ["application/pdf", "text/plain"],
"outputModes": ["application/json"]
}
]
}
MCP: Capabilities are implicitly defined through available resources and methods:

// Client discovers capabilities by querying available methods
{
"method": "tools/list",
"params": {}
}// Response reveals available resources
{
"result": {
"tools": [
{
"name": "document_analyzer",
"description": "Analyzes documents and extracts key information",
"resources": [
"/document_analyzer/extract_entities",
"/document_analyzer/upload_document"
]
}
]
}
}

  1. Task and Workflow Management A2A: Provides explicit task lifecycle management:

// Initiating a document analysis task
{
"type": "SendTaskRequest",
"method": "tasks/send",
"params": {
"description": "Extract entities from quarterly report",
"steps": [
{
"agent_id": "DocumentAnalyzerAgent",
"skill_id": "extractEntities",
"input": [
{
"type": "data",
"data": {
"document_data": "...", // Base64 encoded PDF
"entity_types": ["organization", "person", "date"]
}
}
]
}
]
}
}
// Checking task status
{
"type": "GetTaskRequest",
"method": "tasks/get",
"params": {
"task_id": "task-123456"
}
}
// Response showing task progress
{
"type": "GetTaskResponse",
"result": {
"task_id": "task-123456",
"status": "Running",
"progress": 0.65,
"message": "Extracting entities..."
}
}
MCP: Relies on resource operations with client orchestration:

// Upload document
{
"method": "resources/set",
"params": {
"uri": "/document_analyzer/upload_document",
"content": {
"type": "application/pdf",
"data": "..." // Base64 encoded PDF
}
}
}// Subscribe to status updates
{
"method": "resources/subscribe",
"params": {
"uri": "/document_analyzer/status"
}
}// Response with progress token
{
"result": {
"status": "processing",
"_meta": {
"progressToken": "progress-789"
}
}
}// Server notification of progress
{
"method": "server/notification",
"params": {
"progressToken": "progress-789",
"message": "65% complete: Extracting entities..."
}
}// Request results when ready
{
"method": "resources/get",
"params": {
"uri": "/document_analyzer/extract_entities",
"params": {
"entity_types": ["organization", "person", "date"]
}
}
}

  1. Data Representation and Exchange A2A: Uses flexible message parts with MIME types:

"input": [
{
"type": "data",
"mimeType": "application/json",
"data": {
"query": "What's the total revenue for Q1?",
"parameters": {
"year": 2024,
"precision": "high"
}
}
},
{
"type": "data",
"mimeType": "image/jpeg",
"data": "..." // Base64 encoded chart image
}
]
MCP: Uses specific data types with structured content:

{
"method": "resources/set",
"params": {
"uri": "/financial_analyzer/query",
"content": {
"type": "application/json",
"data": {
"query": "What's the total revenue for Q1?",
"year": 2024,
"precision": "high",
"chart": {
"type": "image/jpeg",
"data": "..." // Base64 encoded chart image
}
}
}
}
}

  1. Error Handling A2A: Defines structured error types:

{
"type": "ErrorResponse",
"error": {
"code": "UnsupportedSkillError",
"message": "The agent does not support the requested skill 'financialProjection'",
"details": {
"availableSkills": ["extractEntities", "summarizeDocument", "answerQuestions"]
}
}
}
MCP: Uses JSON-RPC style error objects:

{
"error": {
"code": -32601,
"message": "Resource not found",
"data": {
"uri": "/financial_analyzer/projection",
"available_resources": [
"/financial_analyzer/query",
"/financial_analyzer/extract",
"/financial_analyzer/summarize"
]
}
}
}

Protocol Structure Examples