adk -a2a-linebot

This MCP server integrates the Agent Development Kit (ADK) with MCP tooling and demonstrates an Agent-to-Agent (A2A) workflow. It exposes multi-agent examples including basic agents, agents that call MCP tools, and a multi-agent system with a router manager and sub-agents. You can experiment with A2A interactions, tool invocation, and orchestration patterns to see how agents collaborate across services and external APIs using the MCP interface. The included structure mirrors practical ADK scenarios: a simple agent, an agent that leverages MCP tools (APIs or external services), a multi-agent arrangement for task delegation, and an A2A interaction example where agents coordinate to complete a joint objective.

Prerequisites:

• Python 3.8+ installed on your system
• Git installed (optional, for cloning)
• Internet access to install dependencies

Step-by-step:

1. Create and activate a Python virtual environment python -m venv .venv source .venv/bin/activate

2. Install required Python packages pip install -r requirements.txt

3. Ensure you have the ADK-MCP server entrypoint available The project uses a module entrypoint, e.g. adk_mcp_server, to run the MCP server. If you have the repository cloned, you should be able to start it with: python -m adk_mcp_server

4. Start the MCP server python -m adk_mcp_server

Note: If you customize environment settings, adjust env vars accordingly before starting the server (see the mcp_config env section).

Optional: If you are deploying, you can containerize or use a process manager to keep the server running in production.

Tips and notes:

• The ADK MCP server demonstrates multiple agent patterns: basic agents, tools-enabled agents, multi-agent coordination, and A2A communications. Use the provided folder structure (1_basic_agent, 2_agent_with_mcp_tools, 3_multi_agents, 4_a2a) as starting points for your experiments.
• Common issues may include missing dependencies or misconfigured environment variables. Ensure you activate the virtual environment before installing and running, and verify that required tools and APIs referenced by agents are accessible.
• If using MCP Tools, you can extend agents to call external APIs or Google Cloud services by implementing tool wrappers in the 2_agent_with_mcp_tools examples.
• For debugging, run with a higher log level (e.g., MCP_LOG_LEVEL=DEBUG) to trace agent decisions, tool selections, and A2A routing decisions.
• When deploying, consider security implications of exposing A2A endpoints and ensure proper authentication/authorization for external agent calls.