The Connectivity Layer: Model Context Protocol (MCP)

Before the Model Context Protocol, developers were forced to write custom "glue code" for every data source or tool integration, leading to redundant work and fragmented implementations. MCP, an open standard developed by Anthropic, solves this by defining a universal protocol for how AI agents (hosts) communicate with external resources.

  • Core Primitives: MCP categorizes integrations into three types: Tools (executable functions like SQL queries or web searches), Resources (data the LLM can read, such as files or databases), and Prompts (reusable templates).
  • Architecture: MCP uses JSON-RPC for communication. Local servers communicate via standard input/output, while remote servers utilize HTTP with streaming support.
  • Key Benefit: It is model-agnostic. Once an MCP server is built for a tool (e.g., GitHub, Slack, Postgres), it can be plugged into any MCP-compatible agent, regardless of the underlying LLM.

The Orchestration Layer: Agent Development Kit (ADK)

While MCP handles external connectivity, the Agent Development Kit (ADK) provides the structural framework for building the agent's internal logic. It treats agent development like traditional software engineering, emphasizing predictability and testability.

  • Core Building Blocks: ADK organizes agents using agents, tools, memory, events, and runners.
  • Execution Control: ADK allows developers to choose between LLM-driven reasoning (flexible) and deterministic workflows (sequential, parallel, or loop-based). This is critical for reliability, as it allows developers to hard-code sequences where model decision-making is unnecessary or risky.
  • State and Memory: ADK manages both short-term session state (working memory) and long-term memory (user preferences), making it easier to build multi-agent systems where a root orchestrator delegates subtasks to specialized agents (e.g., a research agent vs. a writing agent).
  • Debugging: Because the agent suspends at each "yield" point, the runner maintains control, allowing developers to trace behavior and handle consequences before the agent proceeds.

How They Work Together

These technologies occupy different layers of the AI stack and are designed to be used in tandem:

  1. ADK defines the cognition: It handles the planning, reasoning loops, and guardrails that prevent an agent from performing dangerous actions (like deleting a production database).
  2. MCP defines the interface: It provides the standardized "hands" the agent uses to interact with the outside world.

In a practical scenario—such as building a coding assistant—you would use ADK to structure the agent's logic for debugging and testing, and use MCP servers to provide that agent with standardized access to your repository, issue trackers, and test runners.