Building your own MCP server with ADK

Key Concepts

• MCP (Modular Component Protocol): A framework for building modular AI systems, allowing different components (agents, servers, tools) to communicate and interact.
• ADK (Agent Development Kit): A framework for building AI agents.
• MCP Server: A component that exposes tools and capabilities to MCP-compliant clients.
• ADK Tools: Functions or capabilities built using the ADK that can be exposed through an MCP server.
• list_tools Handler: An MCP server handler that informs clients about the available tools.
• call_tool Handler: An MCP server handler that executes a tool when requested by a client.
• Connection Mechanisms: Methods for communication between MCP clients and servers (e.g., stdio, streamable HTTP).
• asyncio: Python's asynchronous I/O framework, fundamental to both ADK and MCP library operations.
• MCP Toolbox for Database: An open-source server for exposing database functionalities as MCP tools.
• Fast MCP: A library that simplifies the creation and deployment of MCP servers.

Building Your Own MCP Server with ADK

This episode focuses on building an MCP server using ADK, enabling any MCP-compliant client to access your tools. This is a step up from the previous episode, which covered connecting ADK agents to existing MCP servers.

The process involves wrapping ADK tools within an MCP server framework. The essential components for building an MCP server with ADK are:

1. MCP Library: A Python library providing the framework to build servers and expose ADK tools.
2. Existing ADK Tools: Pre-built ADK tools, such as function wrappers (e.g., FunctionTool), that can be exposed.
3. Server Handlers:
   • list_tools: This handler advertises the tools available on the server to clients.
   • call_tool: This handler executes a specific tool when invoked by a client.
4. Connection Mechanism:
   • stdio: Suitable for local development and simple deployments.
   • streamable HTTP: Preferred for scalable production environments, allowing network communication.
5. asyncio: Asynchronous I/O is crucial as both ADK and the MCP library are built upon it, requiring server code to be async-first.

The core idea is that the MCP server acts as a wrapper and translator, making ADK tools discoverable and callable by other clients in a standardized manner.

Use Cases for Building MCP Servers

The video explores three distinct use cases:

1. Building a Simple API MCP Server

This use case demonstrates exposing a simple API tool, such as "load web page," through an MCP server.

• Scenario: An agent needs to fetch a web page. Instead of direct ADK calls, the "load web page" tool is exposed via an MCP server.
• Process:
  1. Create a server script (e.g., my_adk_mcp_server.py).
  2. Within the script, use the MCP server to define list_tools and call_tool handlers.
  3. Expose the "load web page" tool in list_tools so clients are aware of its existence.
  4. Implement the call_tool handler to execute the ADK "load web page" tool when a client requests it.
• Testing:
  • Spin up a second ADK agent as a client, connect it to the custom MCP server, discover "load web page," and fetch a website.
  • Alternatively, use ADK web: Create an init.py file, run adv from the parent directory. The ADK client will launch the MCP server as a subprocess, connect, and fetch the page.
• Outcome: This transforms an ADK tool into a standalone MCP service.

2. Building a Database MCP Server

This use case leverages the MCP Toolbox for Database to provide agents with secure access to enterprise data.

• Scenario: Agents require access to enterprise data, but building custom database integrations is complex.
• Solution: Deploy the MCP Toolbox for Database, an open-source server designed to expose database functionalities as MCP tools.
• Integration: Connect an ADK agent using MCP2Set (as discussed in the previous episode).
• Benefit: Agents can securely query and analyze data through the MCP abstraction layer without direct database interaction.

3. Building Custom MCP Servers for Advanced Scenarios

This covers exposing tools that don't fit standard categories or require custom logic.

• Generative Media: Exposing tools like Imagen (image generation) or V (video generation). Google provides open-source MCP servers for these.
• Internal Company APIs: Wrapping private REST APIs to make them usable by other MCP clients.
• Custom Logic: Adding tools for specific tasks, such as mathematical calculations, by defining the logic and making them custom tools.
• Process: The fundamental process remains the same:
  1. Use the MCP library.
  2. Define list_tools and call_tool handlers.
  3. Choose a connection method (stdio for local, streamable HTTP for remote).
• Simplification: The Fast MCP library can simplify building and deploying custom MCP servers.
• Impact: Building custom MCP servers allows for greater creativity and contributes tools to the broader MCP ecosystem, moving beyond consuming existing tools.

Conclusion and Takeaways

The episode concludes by summarizing the key takeaways:

• Building an MCP server with ADK involves wrapping ADK tools using the MCP library and exposing them via list_tools and call_tool handlers.
• Three use cases were covered:
  1. API MCP Server: Exposing simple API tools like "load web page."
  2. Database MCP Server: Utilizing the MCP Toolbox for database access.
  3. Custom MCP Servers: For advanced scenarios like generative media, internal APIs, or custom logic.
• By building custom MCP servers, users empower their ADK agents and make their tools accessible to the entire MCP ecosystem.
• This series (two episodes) has covered both connecting ADK agents to MCP servers and building custom MCP servers, facilitating the transition from isolated agents to an interconnected AI ecosystem.

For more detailed, hands-on instructions, viewers are encouraged to refer to the written tutorial linked in the video description.