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name: Google ADK description: Workflow patterns and gotchas for Google Agent Development Kit. Directs to RAG for implementation.

Google ADK Workflow

When to Choose ADK

• Building for Google Cloud / Vertex AI deployment
• Need streaming-first architecture
• Want agent-to-agent (A2A) protocol support
• Gemini model preference

Decision Framework

Agent Type Selection

Query RAG: mcp__agentic-rag__query_sdk("agent type example", sdk="adk", mode="build")

Critical Gotchas

These are NOT in docs or easy to discover:

1. Export as root_agent - ADK expects this exact name in agent.py
2. Docstrings are MANDATORY for tools - LLM reads them; missing = tool won't work
3. NEVER document ToolContext - It's auto-injected; including it in docstring breaks inference
4. No quotes in .env - GOOGLE_API_KEY=value not GOOGLE_API_KEY="value"
5. Sub-agent descriptions matter - Vague descriptions = poor routing decisions

Workflow: Creating an ADK Agent

Step 1: Project Setup

RAG Query: mcp__agentic-rag__query_sdk("ADK project structure", sdk="adk", mode="explain")

Step 2: Agent Definition

RAG Query: mcp__agentic-rag__query_sdk("LlmAgent basic example", sdk="adk", mode="build")

Step 3: Tool Creation

RAG Query: mcp__agentic-rag__query_sdk("function tool definition", sdk="adk", mode="build")

Remember: Every tool needs a docstring with Args and Returns.

Step 4: State Management (if needed)

RAG Query: mcp__agentic-rag__query_sdk("agent state management", sdk="adk", mode="build")

Step 5: Multi-Agent (if needed)

RAG Query: mcp__agentic-rag__query_sdk("sub_agents routing", sdk="adk", mode="build")

Step 6: Testing

RAG Query: mcp__agentic-rag__query_sdk("ADK testing", sdk="adk", mode="explain")

Common Error Patterns

Advanced Features

Query RAG when you need:

• Streaming: "ADK streaming response"
• A2A Protocol: "agent to agent protocol"
• Visual Builder: "ADK visual builder"
• Callbacks: "ADK callbacks events"
• Memory/Sessions: "ADK session memory"