MCP and A2A comparison table, it is clear at a glance
Master the essence of AI communication protocols and quickly understand the differences between MCP and A2A.
Core content:
1. Background introduction of the MCP and A2A protocol
2. Comparison of core objectives, nature of protocols, and technical architecture
3. Analysis of key abilities, interactive objects, and typical application scenarios
The Agent2Agent (A2A) protocol aims to break the "smart island" and provide a unified and secure communication and collaboration framework for heterogeneous AI Agents. The purpose of the MCP protocol is to communicate the "new and old information world" and provide a unified Secure communication and collaboration framework for AI collaborating with the past. Therefore, it can be inferred that in the future, there will be a protocol between the physical world and the agent, allowing the intelligent body to control a robot or object. However, this is not within the scope of this article.
Comparison table between the MCP and A2A protocols shows the differences and characteristics of the two from the core dimensions:
| Comparison Dimensions | MCP (Model Context Protocol) | A2A (Agent-to-Agent Protocol) |
|---|---|---|
| Background | Led by Anthropic and supported by Microsoft, Meta, OpenAI, etc. (launched in November 2024), it aims to solve the interaction problems between models and external tools/data. | Led by Google and open source (released in April 2025), it focuses on multi-agent collaboration, breaking down information silos and building an AI ecosystem collaboration network. |
| Core Goals | Provide a unified tool and resource calling interface for AI models to solve the problem of "how models connect to the outside world". | Define standardized collaboration rules between intelligent agents to solve the problem of "how to efficiently communicate and allocate tasks among multiple agents". |
| Nature of the Agreement | Similar to the “USB-C interface for AI”, standardizing the interaction between models and tools/data sources. | Similar to "AI diplomatic protocol", it establishes communication rules between intelligent agents. |
| Technical Architecture | Client-server architecture: MCP Server (connecting external resources) and MCP Client (AI application), based on JSON-RPC protocol. | Decentralized P2P architecture: Based on HTTP/SSE/JSON-RPC, supports Agent Card (capability declaration), task lifecycle management ,and other components |
| Core Concepts | Tools, Resources, Sampling, Prompts | Task, Artifact, Message, Agent Card (agent capability description) |
| Solving the problem | 1. High development costs due to differences in model APIs 2. Inconsistent tool calls 3. Difficulty in synchronizing context across models |
1. Inconsistent multi-agent communication protocols 2. Complex task allocation and state synchronization 3. Difficult cross-platform collaboration |
| Key Capabilities | - Unified tool calling interface - Contextual Standardization Management - Security border control |
- Dynamic capability discovery and invocation - Multi-round dialogue management - Distributed task coordination |
| Interactive Objects | Models and tools/data sources (such as databases, APIs) | Agents and agents (such as customer service agents and logistics agents) |
| Multimodal support | Comprehensive support for standardized representation and processing of text, images, audio, and video | Basic support for text, images, and structured data, with flexible format processing |
| Tool calling mechanism | Structured interface defines tool names, parameters, and return values, and enforces standardization | In dynamic declaration and invocation mode, agents can autonomously broadcast capabilities and respond to requests |
| Safety Mechanism | Built-in content filtering, sensitive information processing,and differential privacy verification | Rely on TLS two-way authentication and request signature tamper-proofing, and need to expand the security layer yourself |
| Typical application scenarios | - Knowledge retrieval (such as medical database query) - Single model multi-tool call (such as code generation + document analysis) |
- Multi-agent collaboration (such as a logistics scheduling system ) - Decomposition of complex tasks (e.g., splitting travel planning into weather/hotel/attractions queries) |
| Development complexity | Tool interface development is standardized, suitable for rapid integration of existing model capabilities
|
Need to build a multi-agent collaborative system, suitable for modular team collaborative development |
| Development Status | Industry alliance-led: Supported by mainstream model vendors such as OpenAI and Microsoft, GitHub already has an open-source MCP Server ecosystem with 39k+ Stars | Open source community-driven: Supported by technical partners such as NVIDIA and MongoDB, integrated with frameworks such as LangChain, and natively supported by Google Gemini |
| Complementarity of agreements | Providing a "tool library" (such as the MCP call database) for a single agent, often used as the basic capability for A2A collaboration | Coordinate the division of labor among multiple agents (e.g., A2A assigns tasks to MCP-enhanced agents) to form an ecosystem of "tool layer + collaboration layer" |
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Synergy: MCP and A2A are often used in combination. For example, in a medical scenario, the image analysis agent receives tasks through A2A and then calls the medical database through MCP to complete the special analysis
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Technical Trends: MCP is becoming the de facto standard for model connection tools, while A2A promotes the evolution of multi-agent ecology to the "Internet-like" open network
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Selecting suggestions: Real-time control (such as industrial equipment) is required; MCP must be selected across systems (such as knowledge base synchronization)
