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Building Collaborative AI Agent Ecosystems: A Deep Dive into ADK, MCP & A2A with Pokemon

#googlecloud #adk #mcp #a2a

Introduction: The Future of AI is Collaborative

Imagine asking an AI agent about Pokémon and getting not just basic information, but comprehensive analysis comparing battle statistics, type effectiveness calculations, and fun trivia — all generated through seamless collaboration between specialized AI agents. This isn’t science fiction; it’s what we can build today using Google’s cutting-edge agent technologies.

In this blog post, we’ll explore how to build a production-ready ecosystem of collaborative AI agents using three revolutionary technologies:

  • ADK (Agent Development Kit) — Google’s framework for building intelligent agents

  • MCP (Model Context Protocol)— A standardized way to give agents external tools and capabilities

  • A2A (Agent-to-Agent)— Inter-agent communication and collaboration protocol

We’ll walk through building a complete Pokémon information system that demonstrates these concepts in action, and you’ll learn how to implement similar architectures for your own domains.

Understanding the Core Technologies

ADK: The Agent Development Kit

Google’s Agent Development Kit is a comprehensive framework for building intelligent agents powered by large language models. Think of it as the “operating system” for AI agents — it handles the complex orchestration of LLM interactions, tool integration, and agent lifecycle management.

Key ADK Concepts:

python

# Basic ADK agent structure
agent = LlmAgent(
model="gemini-2.5-flash", # The LLM powering the agent
name="pokemon_agent", # Agent identifier
description="Pokemon information specialist", # What the agent does
instruction=SYSTEM_INSTRUCTION, # The agent's behavioral programming
tools=[tool1, tool2] # External capabilities
)
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ADK abstracts away the complexity of:

  • LLM conversation management

  • Tool invocation and result handling

  • Agent state and memory management

  • Error handling and recovery

  • Performance optimization

MCP: The Model Context Protocol

The Model Context Protocol is a game-changer for AI agent capabilities. Instead of hardcoding functionality into agents, MCP allows you to create modular, reusable “tool servers” that agents can connect to dynamically.

The MCP Architecture:

Agent ←→ MCP Client ←→ HTTP/SSE ←→ MCP Server ←→ External APIs/Services
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Why MCP Matters:

  • Modularity: Tools are separate services, not embedded code

  • Reusability: Multiple agents can use the same tool server

  • Scalability: Tool servers can be deployed independently

  • Security: Tools run in isolated environments with controlled access

Example MCP Tool:

@mcp.tool()
def get_pokemon_info(pokemon_name: str):
"""Get comprehensive information about a Pokemon."""
response = httpx.get(f"https://pokeapi.co/api/v2/pokemon/{pokemon_name}")
return process_pokemon_data(response.json())
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A2A: Agent-to-Agent Communication

Agent-to-Agent communication enables the holy grail of AI systems: specialized agents that can collaborate to solve complex problems. Instead of building monolithic “do-everything” agents, you can create focused specialists that work together.

A2A Communication Patterns:

  1. Direct Collaboration: Agent A requests specific help from Agent B

  2. Intelligent Routing: Master agents delegate tasks to specialists

  3. Collaborative Analysis: Multiple agents contribute to complex queries

# A2A in action
comparison_result = await pokemon_agent.request_analysis_from(
assistant_agent,
"Compare Charizard vs Blastoise stats"
)
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Architecture Deep Dive: The Pokémon Agent Ecosystem

This project is a comprehensive demonstration of an intelligent agent ecosystem specialized in Pokemon information, implemented using three key Google technologies:

  • ADK (Agent Development Kit) — Framework for creating LLM-powered agents
  • MCP (Model Context Protocol) — Protocol for external tools and functionalities
  • A2A (Agent-to-Agent) — Communication and interoperability between agents

Our demonstration system showcases all three technologies working together in a real-world scenario. Here’s how we’ve architected it:

System Components 

🌐 User Interface
↓
🎭 Master Agent (Orchestrator)
↓
🤝 A2A Communication Layer
/ \
↓ ↓
🔵 Pokemon Agent ←→ 🟡 Pokedex Assistant
↓ ↓
📡 Pokemon MCP Server 📊 Analytics MCP Server
↓ ↓
🌍 PokeAPI 🌍 PokeAPI
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🤖 System Agents

Pokemon Agent (pokemon_agent/)

Port: 10001
Specialty: Basic Pokemon information

Features:

  • ✅ Detailed individual Pokemon information
  • ✅ Species data and descriptions
  • ✅ Pokemon search and listing
  • ✅ A2A communication with Pokedex Assistant

MCP Tools:

  • get_pokemon_info(pokemon_name) - Complete Pokemon information
  • get_pokemon_species(pokemon_name) - Species and evolution data
  • search_pokemon(limit, offset) - Paginated search

A2A Capabilities:

  • Can request comparative analysis from Pokedex Assistant
  • Handles analytical query delegations
  • Automatic collaboration for complex responses

Pokedex Assistant (pokedex_assistant/)

Port: 10002
Specialty: Pokemon analysis, comparisons, and team building

Features:

  • 📊 Detailed statistical comparisons
  • ⚔️ Type effectiveness analysis
  • 🎯 Trivia and fun facts generation
  • 📈 Statistical rankings
  • 🏆 Strategic team building
  • 🔍 Team composition analysis
  • ⚡ Team optimization suggestions
  • 🎨 Type coverage calculations

MCP Tools:

Analysis Tools:

  • compare_pokemon_stats(pokemon1, pokemon2) - Statistical comparison
  • calculate_type_effectiveness(attacker_type, defender_types) - Type effectiveness
  • generate_pokemon_trivia(pokemon_name) - Trivia and curiosities
  • get_stat_rankings(stat_name, limit) - Rankings by statistic

Team Building Tools:

  • build_pokemon_team(strategy, team_size) - Create strategic teams
  • analyze_team_composition(pokemon_list) - Analyze team strengths/weaknesses
  • suggest_team_improvements(current_team, strategy) - Optimization suggestions
  • calculate_team_coverage(pokemon_list) - Type coverage analysis

Team Building Strategies:

  • Balanced: Well-rounded teams with good type coverage and stat distribution
  • Offensive: High-damage teams focused on overwhelming opponents
  • Defensive: Tanky teams designed to outlast opponents

A2A Capabilities:

  • Can request basic information from Pokemon Agent
  • Specialized in deep analysis and educational insights
  • Provides advanced strategic insights and team composition advice

Master Agent (master-agent/)

Execution: ADK Web
Specialty: Orchestration and coordination

Features:

  • 🎭 Coordination between specialized agents
  • 🔀 Intelligent query routing
  • 📋 Complex workflow management
  • 🤝 A2A collaboration orchestration

MCP Servers

Pokemon MCP Server (mcp-server/)

Port: 8080
Purpose: Basic Pokemon tools

Available Tools: 

# Basic information
get_pokemon_info(pokemon_name: str) -> Dict
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# Species data
get_pokemon_species(pokemon_name: str) -> Dict
# Search and listing
search_pokemon(limit: int = 20, offset: int = 0) -> Dict
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Analytics MCP Server (analytics-mcp-server/)

Port: 8081
Purpose: Advanced analysis, comparisons, and team building

Available Tools: 

# Statistical comparisons
compare_pokemon_stats(pokemon1: str, pokemon2: str) -> Dict
# Battle analysis
calculate_type_effectiveness(attacker_type: str, defender_types: List[str]) -> Dict
# Trivia generation
generate_pokemon_trivia(pokemon_name: str) -> Dict
# Statistical rankings
get_stat_rankings(stat_name: str, limit: int = 10) -> Dict
# Team building tools
build_pokemon_team(strategy: str = "balanced", team_size: int = 6) -> Dict
analyze_team_composition(pokemon_list: List[str]) -> Dict
suggest_team_improvements(current_team: List[str], strategy: str = "balanced") -> Dict
calculate_team_coverage(pokemon_list: List[str]) -> Dict
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🔄 A2A (Agent-to-Agent) Communication

Communication Protocol

The system implements bidirectional A2A communication between agents:

# Pokemon Agent requesting analysis
comparison = await pokemon_agent.request_pokemon_comparison("Pikachu", "Raichu")
# Pokedex Assistant requesting basic information
basic_info = await assistant_agent.request_pokemon_info("Charizard")
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Collaboration Patterns

  1. Intelligent Delegation
  2. Pokemon Agent delegates analytical queries → Pokedx Assistant
  3. Pokedx Assistant requests basic information → Pokemon Agent
  4. Collaborative Analysis
  5. Combination of basic data + deep analysis
  6. Enriched responses with multiple perspectives
  7. Complex Workflows
  8. Orchestration via Master Agent
  9. Coordinated task sequences

The Power of Specialization

This architecture demonstrates a key principle: specialized agents outperform generalist agents in complex domains. Each agent becomes an expert in its niche, leading to:

  • Better Performance: Focused training and optimization

  • Easier Maintenance: Clear boundaries and responsibilities

  • Scalability: Add new specialists without touching existing agents

  • Reliability: Isolated failures don’t bring down the entire system

Implementation Guide: Building Your Own Agent Ecosystem

Ready to build this system yourself? Let’s walk through the implementation step by step.

Prerequisites

Before we start, ensure you have:

# Python 3.10 or higher
python - version
# UV package manager (faster than pip)
curl -LsSf https://astral.sh/uv/install.sh | sh
# Google API key for Gemini
export GOOGLE_API_KEY="your-api-key-here"
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Step 1: Project Setup 

# Clone and set up the project
git clone https://github.com/falconcr/workshop-adk-a2a.git
cd pokemon-agent
# Install all dependencies
uv sync
# Set up environment variables
cp .env.example .env
# Edit .env with your configuration
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Environment Configuration 

# .env
GOOGLE_GENAI_USE_VERTEXAI=TRUE
GOOGLE_CLOUD_PROJECT=your-project-id
GOOGLE_CLOUD_LOCATION=us-central1
# A2A Agent Configuration
A2A_HOST=localhost
A2A_PORT=10001
A2A_PORT_ASSISTANT=10002
# MCP Server URLs
MCP_SERVER_URL=http://localhost:8080/mcp
ANALYTICS_MCP_SERVER_URL=http://localhost:8081/mcp
# Inter-agent Communication URLs
POKEMON_AGENT_URL=http://localhost:10001
ASSISTANT_AGENT_URL=http://localhost:10002
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Step 2: Build Your First MCP Server

Let’s start with the Pokemon MCP Server that provides basic Pokémon data:

Step 3: Create Your First ADK Agent

Now let’s build the Pokemon Agent that uses our MCP server.

Step 4: Add A2A Communication

Enable your agents to communicate with each other:

# Define Pokemon-related skills
pokemon_info_skill = AgentSkill(
    id='get_pokemon_info',
    name='Pokemon Information Tool',
    description='Get detailed information about a specific Pokemon including stats, abilities, and types',
    tags=['pokemon info', 'pokemon stats', 'pokemon abilities'],
    examples=['Tell me about Pikachu', 'What are the stats for Charizard?'],
)
pokemon_species_skill = AgentSkill(
    id='get_pokemon_species',
    name='Pokemon Species Tool',
    description='Get species information about Pokemon including descriptions and evolution details',
    tags=['pokemon species', 'pokemon description', 'pokemon evolution'],
    examples=['What is the description of Bulbasaur?', 'Tell me about Eevee evolution'],
)
pokemon_search_skill = AgentSkill(
    id='search_pokemon',
    name='Pokemon Search Tool',
    description='Search and list Pokemon with pagination to discover new Pokemon',
    tags=['pokemon search', 'pokemon list', 'discover pokemon'],
    examples=['Show me a list of Pokemon', 'Find Pokemon starting from number 100'],
)
# A2A Agent Card definition
agent_card = AgentCard(
    name='Pokemon Agent',
    description='Helps with Pokemon information, stats, descriptions, and discovery using the PokeAPI',
    url=f'http://{host}:{port}/',
    version='1.0.0',
    defaultInputModes=["text"],
    defaultOutputModes=["text"],
    capabilities=AgentCapabilities(streaming=True),
    skills=[pokemon_info_skill, pokemon_species_skill, pokemon_search_skill],
)
# Make the agent A2A-compatible
a2a_app = to_a2a(root_agent, port=port)
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Step 5: Build the Analytics MCP Server

Create specialized analytical tools.

Step 6: Build the Pokedex

Code

Step 7: Execution

Start MCP servers

# Terminal 1 - Pokemon MCP Server
cd mcp-server
uv run server.py
# Terminal 2 - Analytics MCP Server
cd analytics-mcp-server
uv run server.py
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Start agents

# Terminal 3 - Pokemon Agent
uv run uvicorn pokemon_agent.agent:a2a_app --host localhost --port 10001
# Terminal 4 - Pokedex Assistant
uv run uvicorn pokedex_assistant.agent:a2a_app --host localhost --port 10002
# Terminal 5 - Master Agent
uv run adk web
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Usage Examples

Basic Pokemon Information 

"Tell me about Pikachu"
"What are Charizard's stats?"
"Show me Pokemon starting from number 100
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Pokemon Comparisons 

"Compare Pikachu vs Raichu"
"Which has better stats: Charizard or Blastoise?"
"Analyze the differences between Eevee evolutions"
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Type Effectiveness Analysis 

"How effective is Electric type against Water and Flying?"
"Calculate Fire type effectiveness against Grass/Poison Pokemon"
"What types are super effective against Dragon type?"
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Team Building Commands 

"Build me a balanced Pokemon team"
"Create an offensive team for competitive play"
"I need a defensive team strategy"
"Build a team with 4 Pokemon using offensive strategy"
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Team Analysis 

"Analyze my team: Pikachu, Charizard, Blastoise, Venusaur, Alakazam, Machamp"
"What are the strengths and weaknesses of my team?"
"Evaluate this team composition"
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Team Optimization 

"How can I improve my current team?"
"Suggest better Pokemon for my offensive strategy"
"What changes would make my team more balanced?"
"Suggest improvements for: Garchomp, Rotom, Ferrothorn"
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Type Coverage Analysis 

"Calculate type coverage for my team"
"What types am I missing in my team?"
"Analyze the type balance of my Pokemon team"
"Check coverage for: Charizard, Blastoise, Venusaur, Pikachu"
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Fun Facts and Trivia 

"Generate interesting trivia about Charizard"
"Tell me fun facts about Eevee"
"What are some curious facts about legendary Pokemon?"
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Future Enhancements and Roadmap

Planned Features

  1. Visual Agent Designer*: Drag-and-drop agent workflow creation

  2. Advanced Analytics: Agent performance dashboards and insights

  3. Multi-Modal Support: Image and voice interaction capabilities

  4. Auto-Scaling: Kubernetes-based dynamic agent scaling

  5. Agent Marketplace: Shared repository of specialized agents and MCP tools

Integration Opportunities

  • Langsmith: Enhanced agent observability and debugging

  • Weights & Biases: Agent performance tracking and optimization

  • Ray Serve: Distributed agent deployment and serving

Conclusion: The Agent-Powered Future

The Pokemon agent ecosystem we’ve built demonstrates the transformative potential of collaborative AI systems. By combining ADK’s robust agent framework, MCP’s modular tool architecture, and A2A’s seamless communication protocols, we can create AI systems that are:

  • More Capable: Specialized agents excel in their domains

  • More Maintainable: Clear separation of concerns and responsibilities

  • More Scalable: Independent scaling of different system components

  • More Resilient: Isolated failures don’t cascade through the system

Key Takeaways

  1. Specialization Beats Generalization: Focused agents outperform jack-of-all-trades systems

  2. Modular Tools Scale Better: MCP servers provide reusable, maintainable capabilities

  3. Agent Collaboration Unlocks New Possibilities: A2A communication creates emergent capabilities

  4. Real-World Impact: These patterns apply across industries and use cases

Additional Resources

Documentation

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