Understanding AI Agent Protocols
As artificial intelligence continues to evolve, the need for standardized protocols that enable seamless interaction between AI agents and external systems becomes increasingly critical.
This article explores three pivotal protocols shaping the AI agent ecosystem: Anthropic’s Model Context Protocol (MCP), Google’s Agent-to-Agent Protocol (A2A), and the Agent Development Kit (ADK). Additionally, we’ll delve into the concept of Function Calling and its role within this framework.
Function Calling: Enabling AI to Perform Actions
Function Calling allows AI models to execute predefined functions by generating structured outputs, typically in JSON format. This capability enables AI agents to interact with external tools, APIs, or services, effectively extending their functionality beyond text generation.
Example: An AI assistant can retrieve real-time weather information by invoking a weather API through Function Calling, providing users with up-to-date forecasts.
Model Context Protocol (MCP): Standardizing AI-Tool Integration
Developed by Anthropic, MCP is an open standard designed to facilitate secure, two-way connections between AI models and external data sources or tools. By providing a universal interface, MCP simplifies the integration process, allowing AI agents to access and interact with various systems efficiently.
Use Case: An AI-powered code assistant can utilize MCP to connect with version control systems like GitHub, enabling it to create repositories or manage pull requests directly.
Agent-to-Agent Protocol (A2A): Facilitating AI Agent Collaboration
Introduced by Google, A2A is an open protocol that enables AI agents to communicate and collaborate across different platforms. By standardizing inter-agent communication, A2A allows for the creation of complex, multi-agent workflows where each agent can delegate tasks and share information seamlessly.
Use Case: In a customer service scenario, an AI agent handling customer inquiries can coordinate with another agent responsible for processing returns, ensuring efficient resolution of customer issues.
Agent Development Kit (ADK): Building Comprehensive AI Agents
Google’s ADK provides developers with a toolkit to create AI agents that can both access external tools (via MCP) and collaborate with other agents (via A2A). This comprehensive framework supports the development of robust, enterprise-grade AI agents capable of handling complex tasks and workflows.
Use Case: An enterprise AI agent developed using ADK can manage end-to-end business processes, such as order fulfillment, by interacting with inventory systems, coordinating with logistics agents, and updating customer records.
Comparison: MCP vs A2A vs ADK
| Aspect | Anthropic MCP | Google A2A | Google ADK |
|---|---|---|---|
| Integration Type | Vertical (Agent-to-Tool) | Horizontal (Agent-to-Agent) | Hybrid (Supports Both MCP & A2A) |
| Primary Role | Real-time tool and data access for agents | Multi-agent collaboration and task delegation | Developer toolkit for building agents |
| Architecture | Client-Server (structured APIs) | Peer-to-Peer (intent-based messaging) | Framework + APIs |
| Use Case | CRM access, real-time search, dynamic plugins | Workflow automation, inter-agent negotiation | Creating complex enterprise agents |
| Audience | Developers, API designers | Enterprise IT teams, AI architects | Full-stack developers, system builders |
Conclusion
Understanding the distinct roles of Function Calling, MCP, A2A, and ADK is essential for developing advanced AI systems. Function Calling empowers AI agents to perform specific tasks, MCP standardizes their interaction with external tools, A2A facilitates collaboration among agents, and ADK provides a comprehensive framework for building sophisticated AI agents.
By leveraging these protocols and tools, developers can create AI solutions that are more capable, collaborative, and adaptable to complex workflows.
As AI agents move beyond standalone tools and evolve into autonomous collaborators, the protocols we use become critical infrastructure.
- MCP handles the “how do I get the data I need?”
- A2A solves the “who else can help me complete this workflow?”
- ADK empowers developers to answer both at once.
Together, they form the AI agent stack—a model where tool access, agent collaboration, and modular design are no longer siloed.
