Wireshark MCP

# MCP vs. A2A Protocol Comparison This document provides a detailed comparison between Anthropic's Model Context Protocol (MCP) and Google's Agent-to-Agent (A2A) protocol, explaining how they complement each other in the AI agent ecosystem. ## Overview | Aspect | Model Context Protocol (MCP) | Agent-to-Agent Protocol (A2A) | |--------|------------------------------|-------------------------------| | **Created by** | Anthropic | Google | | **Primary purpose** | Providing tools and context to LLMs | Enabling communication between AI agents | | **Release date** | November 2023 | April 2025 | | **Architecture** | XML-based context windows | JSON-RPC based task system | | **Primary use case** | Enhancing LLM capabilities | Multi-agent collaboration | ## Core Concepts Mapping Both protocols serve important roles in the AI agent ecosystem, but address different aspects of agent functionality: ### MCP Core Concepts - **Context**: The primary unit, providing information to the LLM - **Tools**: Functions that the LLM can call to perform actions - **Input/Output**: Formatted text with XML tags - **Files**: Binary content encoded in Base64 within XML ### A2A Core Concepts - **Tasks**: The primary unit of work with a defined lifecycle - **Agent Card**: Metadata describing agent capabilities and endpoints - **Messages**: Communication turns between agents - **Parts**: Content units (text, data, files) within messages - **Artifacts**: Task outputs with defined structure ## Protocol Structure ### MCP Structure Example ```xml <context> <tool_use> <tool name="calculate"> <parameter name="expression">2+2</parameter> </tool> <result>4</result> </tool_use> <human> Calculate the square root of 16. </human> <system> Use the calculate tool to find the square root of 16. </system> </context> ``` ### A2A Structure Example ```json { "jsonrpc": "2.0", "id": "req-123", "method": "tasks/send", "params": { "id": "task-12345", "message": { "role": "user", "parts": [ { "type": "text", "text": "Calculate the square root of 16." } ] } } } ``` ## Key Differences ### Communication Model **MCP**: - Primarily one-way (from context to model) - Synchronous request-response pattern - Single message per interaction **A2A**: - Bidirectional communication between agents - Supports asynchronous task execution - Streaming updates and push notifications - Multi-turn conversations within a task ### State Management **MCP**: - Largely stateless; context must be provided with each request - No built-in support for persistent sessions - State must be maintained by the application **A2A**: - Stateful task lifecycle (submitted → working → completed/failed) - Tasks have persistent IDs for tracking - Built-in session management - History tracking of state transitions ### Features Comparison | Feature | MCP | A2A | |---------|-----|-----| | **Tools/Skills** | ✅ (Tool definitions) | ✅ (Agent skills) | | **File handling** | ✅ (Base64 in XML) | ✅ (FilePart with URI or bytes) | | **Structured data** | ✅ (XML format) | ✅ (JSON format) | | **Error handling** | ❌ (Limited) | ✅ (JSON-RPC error objects) | | **Authentication** | ❌ (Not specified) | ✅ (Multiple schemes) | | **Discovery** | ❌ (Not specified) | ✅ (Agent Card discovery) | | **Versioning** | ❌ (Not specified) | ✅ (Version in Agent Card) | | **Streaming** | ❌ (Not specified) | ✅ (Server-Sent Events) | | **Push notifications** | ❌ (Not built-in) | ✅ (Webhook support) | | **Session management** | ❌ (Not built-in) | ✅ (SessionID support) | ## Integration Points The protocols can be integrated in several ways: 1. **MCP within A2A**: A2A agents can use MCP to provide context to underlying LLMs 2. **A2A messaging using MCP**: A2A messages can contain MCP-formatted content 3. **Translators**: Services that convert between MCP and A2A formats 4. **Dual-protocol agents**: Agents that support both protocols simultaneously ## When to Use Each Protocol ### Use MCP When: - Working directly with a single LLM like Claude - Providing tools and context to enhance LLM capabilities - Needing to structure input for the LLM to follow - Working in environments that primarily use XML ### Use A2A When: - Building systems with multiple interacting agents - Requiring asynchronous, long-running tasks - Needing to discover agent capabilities dynamically - Building enterprise-ready solutions requiring authentication ### Use Both When: - Building comprehensive agent systems where some components use LLMs - Creating interoperable agents that can work with various AI platforms - Developing agent platforms that need to support different client systems ## Implementation Considerations ### MCP Implementation Tips: - Focus on clean XML structure - Provide concise tool descriptions - Use system prompts to guide tool usage - Consider context length limitations ### A2A Implementation Tips: - Implement well-defined error handling - Use streaming for responsive UIs - Consider task cancellation scenarios - Design clear agent capabilities in the Agent Card ## Protocol Evolution Both protocols are evolving, with future developments likely to include: - **MCP**: Possible expansion to include more standardized tool formats, better file handling, and multi-modal content - **A2A**: Enhanced skills discovery, more complex task routing, and advanced authentication mechanisms ## Conclusion MCP and A2A serve complementary roles in the AI agent ecosystem. MCP excels at providing structured context and tools to LLMs, while A2A provides a framework for agents to collaborate and communicate. By supporting both protocols, Wireshark MCP now offers a comprehensive solution for analyzing and debugging AI agent communications across the ecosystem. The strength of integrating both protocols is the ability to capture the full stack of AI agent interactions, from the low-level LLM context to high-level multi-agent collaborations.