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A2A vs MCP: A Comprehensive Protocol Comparison

A2A vs MCP: A Comprehensive Protocol Comparison

This guide compares the Model Context Protocol (MCP) and the Agent-to-Agent (A2A) protocol, two leading protocols for building and orchestrating AI agents.

Table of Contents

Core Design Philosophies

MCP A2A
Resource-centric architecture with URI-addressable resources Agent-centric design focused on standardized interoperability
Emphasizes flexible data access patterns (get, set, subscribe) Built around structured task management and agent workflows
Provides granular control with client-side workflow orchestration Formalizes agent discovery and capability advertisement
Designed for direct interaction with model capabilities Optimized for orchestrating multi-agent systems

Key Features Comparison

Common Ground

  • JSON-based Communication Structure
  • MIME Type Support for diverse content
  • Asynchronous Operation Support
  • AI Agent Interaction capabilities

Agent Discovery and Capabilities

A2A Approach - "AgentCard" Structure:

{ "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"] } ] }
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MCP Approach - Resource-Based Discovery:

{ "method": "tools/list", "params": {} } // Response { "result": { "tools": [ { "name": "document_analyzer", "description": "Analyzes documents and extracts key information", "resources": [ "/document_analyzer/extract_entities", "/document_analyzer/upload_document" ] } ] } }
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Protocol Structure Examples

MCP: Resource-Based Method Calls

  1. Resource Access Methods 
// GET resource { "method": "resources/get", "params": { "uri": "/sentiment_analyzer/results", "params": { "textId": "doc-12345" } } } // SET resource { "method": "resources/set", "params": { "uri": "/sentiment_analyzer/text", "content": { "type": "text/plain", "data": "I absolutely loved the product, though shipping was a bit slow." } } } // SUBSCRIBE to resource updates { "method": "resources/subscribe", "params": { "uri": "/sentiment_analyzer/stream", "params": { "frequency": "realtime" } } }
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A2A: Agent-Based Task Management

  1. Task Submission 
{ "type": "SendTaskRequest", "method": "tasks/send", "params": { "description": "Translate business proposal to Japanese", "steps": [ { "agent_id": "TranslatorAgent", "skill_id": "translate", "input": [ { "type": "data", "mimeType": "text/plain", "data": "We are pleased to submit our proposal for your consideration." } ], "parameters": { "sourceLanguage": "en", "targetLanguage": "ja", "formality": "formal" } } ] } }
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  1. Multi-Step Task Workflow 
{ "type": "SendTaskRequest", "method": "tasks/send", "params": { "description": "Analyze customer feedback and prepare report", "steps": [ { "step_id": "extract", "agent_id": "DataProcessorAgent", "skill_id": "extractFeedback", "input": [ { "type": "data", "mimeType": "application/json", "data": {"source": "survey_responses.csv", "columns": ["date", "text", "rating"]} } ] }, { "step_id": "analyze", "agent_id": "SentimentAnalyzerAgent", "skill_id": "batchAnalyze", "input": [ { "type": "step_output", "step_id": "extract" } ], "parameters": { "aspects": ["pricing", "quality", "service"], "timeframe": "Q1_2024" } } ] } }
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Advanced Features

MCP: Resource Manipulation and State Management

Complex Resource Structure:

{ "method": "resources/set", "params": { "uri": "/chat_session/context", "content": { "type": "application/json", "data": { "sessionId": "sess-9876", "user": { "id": "user-12345", "preferences": { "language": "en-US", "expertise": "beginner", "verbosity": "detailed" } }, "conversation": { "topic": "Technical Support", "priority": "high", "history": [ {"role": "user", "content": "My application keeps crashing"}, {"role": "assistant", "content": "Let's troubleshoot that. When does it crash?"} ] } } } } }
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A2A: Advanced Task Orchestration

Task Control Operations:

{ "type": "UpdateTaskRequest", "method": "tasks/update", "params": { "task_id": "task-123456", "operation": "pause", "reason": "Waiting for user input on parameter values" } }
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Error Handling

MCP Errors 

{ "error": { "code": -32601, "message": "Method not found", "data": { "requested_method": "resources/delete", "available_methods": ["resources/get", "resources/set", "resources/subscribe"] } } }
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A2A Errors 

{ "type": "ErrorResponse", "error": { "code": "AgentUnavailableError", "message": "The requested agent 'DataProcessorAgent' is currently unavailable", "details": { "estimated_availability": "2024-04-27T18:00:00Z", "alternatives": ["BackupDataProcessorAgent", "LegacyDataProcessorAgent"] } } }
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Subscriptions and Notifications

MCP Subscription Flow 

// Subscribe { "method": "resources/subscribe", "params": { "uri": "/market_data/stock_prices", "params": { "symbols": ["AAPL", "MSFT", "GOOGL"], "interval": "1m" } } } // Real-time update { "method": "server/notification", "params": { "subscriptionId": "sub-abcdef123456", "content": { "timestamp": "2024-04-27T16:42:15Z", "updates": [ {"symbol": "AAPL", "price": 198.42, "change": 0.57}, {"symbol": "MSFT", "price": 412.65, "change": -0.32}, {"symbol": "GOOGL", "price": 167.88, "change": 1.24} ] } } }
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A2A Event Notification 

{ "type": "EventNotification", "event": { "event_type": "TaskStatusChanged", "task_id": "task-567890", "previous_status": "Running", "current_status": "Completed", "timestamp": "2024-04-27T16:45:22Z", "details": { "completion_time": "12.4s", "resource_usage": { "compute_units": 0.0087, "storage_bytes": 25600 } } } }
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Authentication and Security

MCP Authentication 

{ "method": "resources/set", "params": { "uri": "/system/auth", "content": { "type": "application/json", "data": { "api_key": "sk-12345abcdef", "session_id": "sess-678910" } } } }
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A2A Authentication 

{ "type": "AuthRequest", "method": "auth/authenticate", "params": { "agentId": "DataAnalysisAgent", "scheme": "Bearer", "credentials": "eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9..." } }
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Implementation Considerations

Choose MCP when:

  • You need fine-grained control over resource access
  • Your application requires client-side orchestration
  • You prefer a simpler, resource-based approach
  • Your system primarily involves direct client-model interaction

Choose A2A when:

  • You're building a multi-agent ecosystem
  • You need standardized agent discovery and interoperability
  • Your workflows involve complex, multi-step tasks
  • You require formalized task state management

Note: Schemas are being enhanced and changed frequently. This guide will be updated periodically to reflect the latest changes.

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