Prompts

async with client:
    prompts = await client.list_prompts()
    # prompts -> list[mcp.types.Prompt]
    
    for prompt in prompts:
        print(f"Prompt: {prompt.name}")
        print(f"Description: {prompt.description}")
        if prompt.arguments:
            print(f"Arguments: {[arg.name for arg in prompt.arguments]}")
        # Access tags and other metadata
        if hasattr(prompt, '_meta') and prompt._meta:
            fastmcp_meta = prompt._meta.get('_fastmcp', {})
            print(f"Tags: {fastmcp_meta.get('tags', [])}")

async with client:
    prompts = await client.list_prompts()
    
    # Filter prompts by tag
    analysis_prompts = [
        prompt for prompt in prompts 
        if hasattr(prompt, '_meta') and prompt._meta and
           prompt._meta.get('_fastmcp', {}) and
           'analysis' in prompt._meta.get('_fastmcp', {}).get('tags', [])
    ]
    
    print(f"Found {len(analysis_prompts)} analysis prompts")

async with client:
    # Simple prompt without arguments
    result = await client.get_prompt("welcome_message")
    # result -> mcp.types.GetPromptResult
    
    # Access the generated messages
    for message in result.messages:
        print(f"Role: {message.role}")
        print(f"Content: {message.content}")

async with client:
    # Prompt with simple arguments
    result = await client.get_prompt("user_greeting", {
        "name": "Alice",
        "role": "administrator"
    })
    
    # Access the personalized messages
    for message in result.messages:
        print(f"Generated message: {message.content}")

from dataclasses import dataclass

@dataclass
class UserData:
    name: str
    age: int

async with client:
    # Complex arguments are automatically serialized
    result = await client.get_prompt("analyze_user", {
        "user": UserData(name="Alice", age=30),     # Automatically serialized to JSON
        "preferences": {"theme": "dark"},           # Dict serialized to JSON string
        "scores": [85, 92, 78],                     # List serialized to JSON string
        "simple_name": "Bob"                        # Strings passed through unchanged
    })

async with client:
    result = await client.get_prompt("data_analysis", {
        # These will be automatically serialized to JSON strings:
        "config": {
            "format": "csv",
            "include_headers": True,
            "delimiter": ","
        },
        "filters": [
            {"field": "age", "operator": ">", "value": 18},
            {"field": "status", "operator": "==", "value": "active"}
        ],
        # This remains a string:
        "report_title": "Monthly Analytics Report"
    })

async with client:
    result = await client.get_prompt("conversation_starter", {"topic": "climate"})
    
    # Access individual messages
    for i, message in enumerate(result.messages):
        print(f"Message {i + 1}:")
        print(f"  Role: {message.role}")
        print(f"  Content: {message.content.text if hasattr(message.content, 'text') else message.content}")

async with client:
    # Raw MCP method returns full protocol object
    prompts_result = await client.list_prompts_mcp()
    # prompts_result -> mcp.types.ListPromptsResult
    
    prompt_result = await client.get_prompt_mcp("example_prompt", {"arg": "value"})
    # prompt_result -> mcp.types.GetPromptResult

async with client:  # Multi-server client
    # Prompts from any server are directly accessible
    result1 = await client.get_prompt("weather_prompt", {"city": "London"})
    result2 = await client.get_prompt("assistant_prompt", {"query": "help"})

async with client:
    result = await client.get_prompt("system_configuration", {
        "role": "helpful assistant",
        "expertise": "python programming"
    })
    
    # Access the returned messages
    message = result.messages[0]
    print(f"Prompt: {message.content}")

async with client:
    result = await client.get_prompt("interview_template", {
        "candidate_name": "Alice",
        "position": "Senior Developer"
    })
    
    # Multiple messages for a conversation flow
    for message in result.messages:
        print(f"{message.role}: {message.content}")