AI agents are becoming increasingly capable collaborators, but on their own, even the most sophisticated language models (LLMs) have limitations. While LLMs are excellent at generating text, they cannot take independent actions or access live data unless they are properly connected to external tools. To truly serve as practical assistants in the real world, AI agents need to communicate with external systems and with each other. This is where two emerging protocols come into play: Anthropic’s Model Context Protocol (MCP) and Google’s Agent-to-Agent (A2A). These protocols serve as the “glue” that connects AI agents to the broader ecosystem, enabling them to go beyond isolation and become genuinely useful.
In this post, we will explore both MCP and A2A in detail. First, we’ll examine MCP and how it streamlines the integration of AI tools, making it easier to connect language models to various applications. Then, we’ll delve into A2A and how it enables AI agents to collaborate and share information seamlessly. We will also provide practical examples of these protocols in action, particularly in the context of a Web3 investment platform that uses MCP for tool integration and A2A for agent coordination.
Anthropic’s Model Context Protocol (MCP)
MCP, developed by Anthropic, is an open standard introduced in late 2024 to address a key challenge in AI: ensuring that agents can consistently access the necessary data, tools, and context to perform their tasks. Simply put, MCP establishes a standardized method for AI agents to interact with external resources, preventing them from becoming isolated. Think of MCP as a “universal connector” for AI applications, similar to how USB-C standardized device connections. This universal approach simplifies the process of integrating different tools and services with AI models.
Before MCP, integrating AI with external systems was often cumbersome. Developers had to write custom code or use specific frameworks for each new tool or service. Each integration required a unique connector, creating a fragmented system. Tools like LangChain tried to address this, but still left room for custom solutions. MCP solves this by offering a standardized protocol for integration, so any MCP-compliant AI client can communicate with any compliant tool or service without needing custom code for each one.
This standardization is valuable for businesses because it accelerates the development of AI-powered applications. Instead of writing new code for every new integration, developers can use MCP to plug different tools and data sources into their AI systems easily. Additionally, MCP future-proofs AI systems by allowing them to quickly switch between different data sources or models without needing to rebuild integration pipelines from scratch. Flexibility, scalability, and security are key benefits of MCP, which ensures that AI agents always have the right context when needed.
How MCP Works: The Process Flow
- MCP Client (AI Side): This is typically the AI agent or application that needs data or tools, such as a chatbot or virtual assistant. The client is responsible for initiating requests to fetch information or invoke specific actions.
- MCP Server (Tool/Data Side): The MCP server is the service that exposes a specific tool or data resource to the AI. It could be anything from a database server to an API like a weather service. The server communicates using the same protocol, ensuring that all interactions are standardized and seamless.
Google’s Agent-to-Agent (A2A) Protocol
Next, let’s turn our attention to the Agent-to-Agent (A2A) protocol developed by Google. Unlike MCP, which focuses on connecting AI agents to external tools, A2A enables communication and collaboration between different AI agents. Launched in late 2024, A2A is designed to facilitate the coordination of AI agents across various platforms and applications. As organizations deploy more AI agents to handle tasks like customer support, analytics, and IT automation, A2A helps agents from different vendors or systems work together efficiently.
A2A is built around the concept of interoperability. Imagine an office scenario where multiple agents handle different tasks: one manages calendars, another tracks customer data, and another monitors network security. Traditionally, these agents would operate independently, but with A2A, they can communicate and collaborate seamlessly. For example, a calendar agent could request security logs from a monitoring agent when scheduling a meeting, or a sales agent could retrieve financial data from a finance agent. The key here is that A2A allows agents from different sources (like OpenAI, Anthropic, or custom in-house systems) to work together, provided they follow the A2A protocol.
How A2A Works and What It Enables
At its core, A2A defines a set of rules for how two agents—one initiating a request (the client) and another responding (the remote agent)—should interact. For instance, a project management agent (client) might ask a calendar agent (remote) to schedule a meeting. This interaction is structured through tasks that go through various stages: creation, in-progress, and completion. Each task has a unique ID and includes status updates as the task progresses.
A2A also introduces the concept of “Agent Cards,” which allow agents to advertise their capabilities. When one agent needs assistance, it can query a network of agents and retrieve their “cards” to see which one can handle the requested task. An Agent Card is a simple profile that specifies what the agent can do, like “I can schedule events” or “I can manage customer databases.” This feature makes it easy for agents to discover one another dynamically, without hardcoded knowledge of each other’s capabilities.
In practical terms, A2A enables agents to collaborate on complex workflows. For example, in a hiring scenario, a hiring manager’s main agent might use A2A to call a recruiter agent to search for candidate resumes, a scheduler agent to arrange interviews, and a background-check agent to verify credentials. The hiring manager simply observes the end-to-end process, while the agents handle specific tasks in parallel.
Importantly, A2A is framework-agnostic. It works across different AI systems, whether built on OpenAI, Google, or Anthropic platforms. As long as the agents use A2A, they can communicate, just like email allows different systems to send and receive messages regardless of the software used.
Conclusion
In summary, both MCP and A2A play crucial roles in the evolution of AI communication. MCP standardizes how AI agents connect to external tools and services, streamlining integrations and ensuring that the right context is always available. On the other hand, A2A allows AI agents to work together, enhancing collaboration and enabling more complex, multi-agent workflows. Together, these protocols are helping to make AI agents more powerful, flexible, and efficient, laying the groundwork for the next generation of intelligent, collaborative systems.
