Google ADK and Anthropic MCP: A comparative review

At Griffin AI, we're deeply interested in technologies shaping the future of AI agent design. Two particularly noteworthy frameworks have recently captured our attention: Google's Agent Development Kit (ADK) and Anthropic's Model Context Protocol (MCP). Each address critical but distinct areas of AI agent development, so understanding their strengths, architectures, and potential integration points is valuable for any team aiming to build advanced AI systems.

Notably, both ADK and MCP are fully open-source. Google’s Agent Development Kit is released under the Apache 2.0 license and available on GitHub as a code-first Python toolkit for building, evaluating, and deploying sophisticated AI agents. Anthropic’s Model Context Protocol is similarly maintained as an open-source initiative, with multiple SDKs and server implementations already published. 

This level of openness matters. These are foundational infrastructures that will help define how AI agents behave, interact, and scale. Open-sourcing them ensures transparency and auditability, and it invites a wider community to shape their evolution. At a moment when trust in AI depends on openness and verifiability, the decisions of these companies to make tools like these fully open source stand as powerful and important precedents.

Why Google's ADK matters 

Google’s Agent Development Kit provides a comprehensive platform for creating and orchestrating AI agents. It's designed to simplify the complexity involved in agent development, offering tools that handle everything from agent logic to multi-agent coordination. 

Flexible orchestration capabilities 

ADK includes multiple orchestration strategies. Workflow agents can execute tasks sequentially, in parallel, or through looping structures. For more dynamic scenarios, ADK allows decision-making through language model-driven routing, enabling agents to adapt responses based on evolving context. 

Hierarchical agent architectures 

One powerful aspect of ADK is its ability to create multi-agent systems. Developers can design hierarchies where specialized agents collaborate, delegate tasks, and communicate effectively. This modularity makes managing complex tasks more practical and efficient. 

Extensive tooling ecosystem 

ADK provides extensive built-in tooling such as Search and Code Execution functions. It also supports integration with third-party tools like LangChain and LlamaIndex, allowing developers considerable flexibility when extending agent functionality. 

Robust deployment and evaluation framework 

Deployment in ADK is well-supported through multiple pathways, including local environments, containerized deployments with Docker, or scaled cloud environments using Google’s Vertex AI platform. Moreover, ADK features built-in evaluation tools that measure agent performance systematically, which helps teams continuously improve agent accuracy and response quality. 

Developer considerations 

Although ADK emphasizes clear, Pythonic APIs to improve developer productivity, some complexities remain, especially around asynchronous operations. Developers have noted challenges in conversation management and asynchronous workflows, which require careful handling to avoid overly complicated implementations. 

Anthropic's MCP and its role in agent systems 

Anthropic's Model Context Protocol is an open standard designed specifically to streamline how AI models access external data sources and tools. MCP simplifies integration, making it easier for agents to dynamically connect and interact with a wide range of resources without individual, custom-built integrations. 

Clear and structured architecture 

MCP is based on a clearly defined client-server architecture. Applications or "Hosts" initiate user interactions, while "Clients" manage connections to MCP "Servers." Servers expose data and functionality through standardized structures known as Tools, Resources, and Prompts. 

Universal connectivity across services 

A significant advantage of MCP is the standardized communication it enables between AI agents and external data. MCP supports multiple transport methods including Standard Input/Output (STDIO) and Server-Sent Events (SSE), making it adaptable to various integration needs, from local desktop tools to remote cloud services. 

Improved development efficiency 

By offering a universal connector between agents and external services, MCP significantly reduces development time. Teams no longer need to create bespoke connections for each data source, API, or service, freeing up resources for other critical areas like agent logic and user interaction. 

Security and scalability considerations 

Despite its advantages, MCP brings certain challenges. It establishes stateful connections that require careful management, particularly when scaling systems. Ensuring secure access to sensitive resources also demands robust authentication methods, something developers must implement thoughtfully. 

Comparing ADK and MCP: Complementary strengths 

ADK and MCP address different, yet complementary aspects of agent development. While ADK focuses on agent orchestration, reasoning, and internal structure, MCP simplifies external data access and standardizes how agents communicate with external services. 

Areas of architectural focus 

ADK serves as the core logic layer for agents, handling decision-making, orchestration, and internal operations. MCP, in contrast, standardizes interactions between agents and external resources, ensuring reliable data access and streamlined integrations. 

Integration possibilities between ADK and MCP 

Integration between ADK and MCP can significantly enhance overall system capabilities. For example, an ADK-built agent might leverage MCP servers for specialized tasks such as fetching live data or triggering actions on external platforms. Similarly, tools developed in ADK can themselves be exposed as MCP servers, expanding their utility across broader agent ecosystems. 

Real-world integration example: Flight search scenario 

One practical scenario illustrating ADK-MCP integration involves flight search assistance. Here, an ADK agent can handle user interactions, while delegating the actual flight search functionality to an MCP server. The MCP server queries real-time flight data APIs and returns structured results to the ADK agent. The agent then presents these results coherently to users, demonstrating how ADK and MCP can effectively work together. 

Implementation challenges when combining frameworks 

Developers considering ADK-MCP integration need to carefully manage several areas, particularly asynchronous programming, connection lifecycles, session management, and security authentication. Both frameworks rely on asynchronous operations, requiring expertise in managing async workflows smoothly to avoid complexity. 

Use cases and recommendations for developers 

Understanding where ADK and MCP each excel helps developers match their needs effectively. 

Enterprise-grade AI systems 

Google ADK is particularly strong in enterprise scenarios due to its scalability, built-in evaluation tools, and robust orchestration features. Combining ADK with MCP for standardized data access can further streamline development for large-scale deployments. 

AI-powered applications and assistants 

For applications requiring rapid external data access, MCP’s standardized integration approach can be particularly beneficial. Leveraging ADK alongside MCP allows teams to build AI assistants with both sophisticated internal logic and easy connectivity to external APIs and databases. 

Multi-vendor and interoperable environments 

MCP’s ability to act as a universal connector makes it highly suited for multi-vendor scenarios. For organizations needing interoperability between diverse AI models, MCP provides a straightforward integration point. Meanwhile, ADK’s robust agent-building framework allows development of intelligent orchestrators to manage complex multi-agent interactions effectively. 

Outlook from Griffin AI's perspective 

The evolution of these frameworks reflects broader industry trends towards standardization, interoperability, and modular design in AI agent development. Google's ADK and Anthropic's MCP, each addressing critical components of AI system architecture, illustrate how agent technology is rapidly maturing. As they continue developing, we anticipate broader integration patterns emerging, simplifying the creation of sophisticated AI solutions even further. 

Exploring these technologies enriches Griffin AI’s understanding of modern agent architectures, multi-agent ecosystems, and standardized integration patterns. Both ADK and MCP highlight principles of modularity, flexibility, and scalability, values that align closely with Griffin AI’s ongoing focus on decentralized and adaptive AI agent development. 

For a practical look into the kinds of decentralized, adaptable agents Griffin AI is currently building, explore our AI Agent Playground.