Petstore MCP Server & Client

A comprehensive Model Context Protocol (MCP) implementation for the Swagger Petstore API. This project includes both a complete MCP server and a sophisticated client system for seamless agent integration.

Overview

This project provides:

• MCP Server: Complete implementation of all Petstore API endpoints
• MCP Client: High-level client with agent-friendly interfaces
• Agent Integration: Ready-to-use components for AI agents
• Configuration Management: Flexible configuration system
• Prompt Templates: Pre-built prompts for different scenarios

Project Structure

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

---

MCP Server

Features

The MCP server provides comprehensive access to the Petstore API with 19 tools across three categories:

Pet Management (8 tools)

• add_pet: Add a new pet to the store
• update_pet: Update an existing pet
• get_pet_by_id: Find pet by ID
• find_pets_by_status: Find pets by status (available, pending, sold)
• find_pets_by_tags: Find pets by tags
• update_pet_with_form: Update a pet using form data
• delete_pet: Delete a pet
• upload_pet_image: Upload an image for a pet

Store Operations (4 tools)

• get_inventory: Get pet inventories by status
• place_order: Place an order for a pet
• get_order_by_id: Find purchase order by ID
• delete_order: Delete purchase order by ID

User Management (7 tools)

• create_user: Create a new user
• create_users_with_list: Create multiple users from a list
• login_user: Log user into the system
• logout_user: Log out current user session
• get_user_by_name: Get user by username
• update_user: Update user information
• delete_user: Delete a user

Server Installation

1. Install server dependencies:

   pip3 install -r requirements.txt
   

2. Make the server executable:

   chmod +x petstore-mcp-server.py
   

3. Or run the setup script:

   bash setup.sh
   

Server Configuration

For Amazon Q CLI

Add the server to your MCP configuration:

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

Running the Server

# Direct execution
python3 petstore-mcp-server.py

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

Server API Examples

Pet Management

Add a new pet:

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

Find pets by status:

{
  "status": "available"
}

Store Operations

Place an order:

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

User Management

Create a user:

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

---

MCP Client

Client Architecture

The MCP client system consists of multiple layers for maximum flexibility and ease of use:

Core Components

1. Transport Layer (transport.py)

   • Handles MCP server communication
   • Connection management with async context managers
   • Error handling and logging

2. Configuration Management (client_config.py)

   • Centralized configuration system
   • Server connection settings
   • Retry policies and caching options

3. Prompt Management (prompt_manager.py)

   • Template-based prompt generation
   • Different templates for various operations
   • Extensible prompt system

4. Sampling Configuration (sampling.py)

   • Multiple AI model sampling presets
   • Configurable parameters for different use cases
   • Easy configuration management

5. Agent Interface (agent_interface.py)

   • High-level task execution
   • Seamless integration of all components
   • Agent-friendly API

Client Installation

1. Install client dependencies:

   pip3 install -r client_requirements.txt
   

2. Ensure server is available:

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

Client Usage

Basic Client Usage

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()

Agent Interface Usage

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")

Advanced Client Features

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()

Configuration Options

Client Configuration

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
)

Sampling Configurations

Available sampling presets:

• conservative: Low temperature, focused responses
• balanced: Moderate creativity and focus (default)
• creative: Higher temperature, more creative responses
• precise: Zero temperature, deterministic responses

from sampling import SamplingManager

sampling = SamplingManager()

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

Prompt Templates

Available prompt templates:

• pet_search: For finding and filtering pets
• pet_management: For pet inventory operations
• order_processing: For handling customer orders
• user_management: For user account operations

from prompt_manager import PromptManager

prompts = PromptManager()

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

Agent Integration

Task-Based Operations

The agent interface provides high-level tasks that AI agents can easily use:

# 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")

Workflow Execution

# 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")

Error Handling

The client system includes comprehensive error handling:

• Network Errors: Automatic retry with exponential backoff
• API Errors: Meaningful error messages and suggestions
• Validation Errors: Input validation with helpful feedback
• Connection Errors: Graceful degradation and recovery

Testing

Server Testing

# Test server functionality
python3 test_server.py

Client Testing

# Test client functionality
python3 example_usage.py

API Reference

Base URL

• Production: https://petstore3.swagger.io/api/v3

Authentication

• API Key authentication for certain endpoints
• OAuth2 support for pet operations

Rate Limiting

• Configurable retry policies
• Exponential backoff for failed requests

Development

Extending the Server

1. Add new tool functions using @server.call_tool() decorator
2. Update tool definitions in handle_list_tools()
3. Add appropriate error handling and validation
4. Update documentation

Extending the Client

1. Add new methods to PetstoreClient class
2. Create corresponding agent workflows
3. Add prompt templates for new operations
4. Update configuration options

Adding New Prompts

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)

Security Considerations

• API keys are handled securely
• Passwords are not logged or cached
• HTTPS connections for all API calls
• Input validation and sanitization
• Error messages don't expose sensitive information

Performance

• Async/await throughout for non-blocking operations
• Connection pooling for HTTP requests
• Configurable caching with TTL
• Efficient JSON parsing and serialization

Contributing

1. Fork the repository
2. Create a feature branch
3. Add tests for new functionality
4. Update documentation
5. Submit a pull request

License

This project follows the same license as the Swagger Petstore API (Apache 2.0).

Support

For issues and questions:

1. Check the example usage scripts
2. Review the test files
3. Examine the configuration options
4. Create an issue with detailed information