MCP vs ADK: How Modern AI Agents Connect and Work Together

As developers increasingly build AI agents capable of performing actions, they face two fundamental challenges: enabling agents to communicate with external tools and data, and structuring the agents themselves for reliable orchestration. The Model Context Protocol (MCP) and the Agent Development Kit (ADK) are two key technologies that address these distinct problems.

MCP: The Connectivity Protocol

The Model Context Protocol (MCP), an open standard from Anthropic, solves the problem of connectivity. Before MCP, connecting an LLM to resources like databases, APIs, or local files required writing custom, non-reusable “glue code” for each integration. MCP standardizes this interface, defining a clear protocol for how a client (the AI agent or “host”) communicates with a server that exposes a tool or data source.

Key aspects of MCP include:

• Standardized Communication: It uses JSON-RPC as its message format. For local servers (e.g., an IDE plugin accessing a file system), it uses standard I/O. For remote servers, it uses HTTP with streaming support and authentication tokens.
• Core Primitives: MCP is built on three main concepts:
  • Tools: Functions the LLM can call, such as run_sql_query or search_web.
  • Resources: Data the LLM can read, like files, documentation, or database records.
  • Prompts: Reusable, pre-built prompt templates exposed by the server.
• Model-Agnostic and Reusable: An MCP server is a wrapper around a tool that can be written once and used by any MCP-compatible client, regardless of the underlying LLM (Claude, GPT, Gemini, etc.). This “plug and play” nature saves significant development time. A growing ecosystem of MCP servers already exists for common tools like GitHub, Slack, Google Drive, and Postgres.

ADK: The Orchestration Framework

The Agent Development Kit (ADK), an open-source Python framework from Google, addresses the second challenge: how to build, structure, and run agents in a predictable and testable way. It provides a full-stack system for creating reliable, multi-agent architectures.

ADK’s structure is built on several core components:

• Defined Execution Units: In ADK, an agent is not just a prompt. It is a formal unit with a specific model, instructions, a set of allowed tools, and a controlled reasoning loop.
• The Runner: A central component that orchestrates the agent’s lifecycle. The runner passes a user query to an agent. The agent processes the query and then “yields” back a tool call request or a state change. The runner executes the request, commits the change to the session state, and then resumes the agent. This loop provides full control, making tracing and debugging behavior significantly easier.
• State and Memory: ADK provides proper state management, distinguishing between short-term state (working memory within a single conversation) and long-term memory (persistent information across sessions, like user preferences).
• Diverse Agent Types: ADK supports multiple types of agents, allowing developers to choose the right tool for the job:
  • LLM-driven agents for flexible, classic “think and act” reasoning loops.
  • Deterministic workflow agents (sequential, parallel, loop) for tasks that require a strict, hard-coded sequence of steps, which enhances reliability.
  • Custom agents.

Complementary, Not Competing: How They Work Together

MCP and ADK are not competitors; they are complementary technologies that operate at different layers of the AI agent stack.

• ADK defines what the agent should do. It structures the agent’s internal cognition, planning, reasoning loop, and tool orchestration.
• MCP defines how the agent communicates with the external world to perform those actions. It is the protocol layer for tool use.

Consider a coding assistant built to search a repository, run tests, and manage issues.

• ADK would be used to structure the agent’s behavior: its reasoning for when to search the repo versus run a test, its retry logic upon failure, and the guardrails to prevent destructive actions (like deleting a production database).
• MCP would provide the standardized interfaces for the agent to communicate with the necessary tools—an MCP server for the Git repository, another for the test runner, and a third for the issue tracker.

In essence, ADK is the framework for building the agent’s brain, while MCP provides the universal language for that brain to talk to the outside world. An ADK agent can seamlessly use any MCP server as a tool source, combining structured orchestration with standardized connectivity.

• Tags:
• AI Agents
• Feature
• MCP
• Multi-Agent Systems
• Orchestration
• ADK
• Tool Integration