petstore/
├── openapi.yaml              # OpenAPI 3.0 specification
├── petstore-mcp-server.py    # MCP server implementation
├── petstore_mcp_client.py    # Comprehensive MCP client
├── agent_interface.py        # High-level agent interface
├── transport.py              # MCP transport layer
├── prompt_manager.py         # Prompt template management
├── sampling.py               # AI model sampling configurations
├── client_config.py          # Configuration management
├── requirements.txt          # Server dependencies
├── client_requirements.txt   # Client dependencies
├── mcp-server-config.json    # MCP server configuration
├── example_usage.py          # Usage examples
├── test_server.py            # Server testing script
├── setup.sh                  # Setup script
└── README.md                 # This documentation

pip3 install -r requirements.txt

chmod +x petstore-mcp-server.py

bash setup.sh

{
  "mcpServers": {
    "petstore": {
      "command": "python3",
      "args": ["petstore-mcp-server.py"],
      "cwd": "/path/to/petstore",
      "env": {}
    }
  }
}

# Direct execution
python3 petstore-mcp-server.py

# With Amazon Q CLI
q chat --mcp-server petstore

{
  "pet": {
    "name": "Buddy",
    "photoUrls": ["https://example.com/buddy.jpg"],
    "category": {
      "id": 1,
      "name": "Dogs"
    },
    "tags": [
      {
        "id": 1,
        "name": "friendly"
      }
    ],
    "status": "available"
  }
}

{
  "status": "available"
}

{
  "order": {
    "petId": 123,
    "quantity": 1,
    "shipDate": "2024-12-01T10:00:00Z",
    "status": "placed",
    "complete": false
  }
}

{
  "user": {
    "username": "johndoe",
    "firstName": "John",
    "lastName": "Doe",
    "email": "john@example.com",
    "password": "password123",
    "phone": "555-1234",
    "userStatus": 1
  }
}

pip3 install -r client_requirements.txt

# Make sure the MCP server is in the same directory
   ls petstore-mcp-server.py

from petstore_mcp_client import PetstoreClient

async def main():
    client = PetstoreClient()
    
    async with client.connect():
        # Find available pets
        pets = await client.find_pets_by_status("available")
        
        # Add a new pet
        new_pet = await client.add_pet(
            name="Buddy",
            photo_urls=["https://example.com/buddy.jpg"],
            status="available"
        )
        
        # Get inventory
        inventory = await client.get_inventory()

from agent_interface import PetstoreAgent
from client_config import ClientConfig

async def main():
    # Initialize agent with configuration
    config = ClientConfig.default()
    agent = PetstoreAgent(config)
    
    # Execute high-level tasks
    result = await agent.execute_task("find_pets", status="available")
    
    # Get prompts for AI models
    prompt = agent.get_prompt("pet_search", status="available", tags=["friendly"])
    
    # Get sampling configuration
    sampling_config = agent.get_sampling_config("balanced")

from petstore_mcp_client import PetstoreAgent

async def main():
    agent = PetstoreAgent()
    
    # Execute complex workflows
    workflow_result = await agent.execute_pet_workflow(
        "create_pet",
        name="Max",
        category="Dogs",
        tags=["friendly", "large"]
    )
    
    # Get store summary
    summary = await agent.client.get_store_summary()

from client_config import ClientConfig, ServerConfig

# Custom configuration
config = ClientConfig(
    server=ServerConfig(
        command="python3",
        args=["./petstore-mcp-server.py"],
        timeout=30
    ),
    retry_attempts=3,
    retry_delay=1.0,
    log_level="INFO",
    enable_caching=True,
    cache_ttl=300
)

from sampling import SamplingManager

sampling = SamplingManager()

# Get different configurations
conservative = sampling.get_config_dict("conservative")
creative = sampling.get_config_dict("creative")

from prompt_manager import PromptManager

prompts = PromptManager()

# Get prompt for pet search
prompt = prompts.get_prompt(
    "pet_search",
    status="available",
    tags=["friendly", "small"]
)

# Find pets
await agent.execute_task("find_pets", status="available", tags=["friendly"])

# Manage pets
await agent.execute_task("manage_pet", action="add", name="Buddy", photoUrls=["url"])

# Process orders
await agent.execute_task("process_order", action="place", petId=123, quantity=1)

# Manage users
await agent.execute_task("manage_user", action="create", username="john", email="john@example.com")

# Pet management workflow
result = await agent.execute_pet_workflow(
    "create_pet",
    name="Luna",
    category="Cats",
    tags=["indoor", "quiet"],
    photo_urls=["https://example.com/luna.jpg"]
)

# Inventory management workflow
inventory = await agent.execute_pet_workflow("manage_inventory")

# Test server functionality
python3 test_server.py

# Test client functionality
python3 example_usage.py

from prompt_manager import PromptTemplate

# Create new template
template = PromptTemplate(
    system="You are a pet care specialist.",
    user_template="Provide care advice for {pet_type} with {condition}",
    examples={"basic": "Care for a sick dog"}
)

# Add to manager
prompt_manager.add_template("pet_care", template)