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New in version 2.0.0 Use this when you need to retrieve server-defined message templates for LLM interactions. Prompts are reusable message templates exposed by MCP servers. They can accept arguments to generate personalized message sequences for LLM interactions.

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Basic Usage

Request a rendered prompt with get_prompt(): 
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}")
Pass arguments to customize the prompt: 
async with client:
    result = await client.get_prompt("user_greeting", {
        "name": "Alice",
        "role": "administrator"
    })

    for message in result.messages:
        print(f"Generated message: {message.content}")

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Argument Serialization

New in version 2.9.0 FastMCP automatically serializes complex arguments to JSON strings as required by the MCP specification. You can pass typed objects directly: 
from dataclasses import dataclass

@dataclass
class UserData:
    name: str
    age: int

async with client:
    result = await client.get_prompt("analyze_user", {
        "user": UserData(name="Alice", age=30),     # Automatically serialized
        "preferences": {"theme": "dark"},           # Dict serialized
        "scores": [85, 92, 78],                     # List serialized
        "simple_name": "Bob"                        # Strings unchanged
    })
The client handles serialization using pydantic_core.to_json() for consistent formatting. FastMCP servers automatically deserialize these JSON strings back to the expected types.

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Working with Results

The get_prompt() method returns a GetPromptResult containing a list of messages: 
async with client:
    result = await client.get_prompt("conversation_starter", {"topic": "climate"})

    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}")
Prompts can generate different message types. System messages configure LLM behavior: 
async with client:
    result = await client.get_prompt("system_configuration", {
        "role": "helpful assistant",
        "expertise": "python programming"
    })

    # Typically returns messages with role="system"
    system_message = result.messages[0]
    print(f"System prompt: {system_message.content}")
Conversation templates generate multi-turn flows: 
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}")

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Version Selection

New in version 3.0.0 When a server exposes multiple versions of a prompt, you can request a specific version: 
async with client:
    # Get the highest version (default)
    result = await client.get_prompt("summarize", {"text": "..."})

    # Get a specific version
    result_v1 = await client.get_prompt("summarize", {"text": "..."}, version="1.0")
See Metadata for how to discover available versions.

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Multi-Server Clients

When using multi-server clients, prompts are accessible directly without prefixing: 
async with client:  # Multi-server client
    result1 = await client.get_prompt("weather_prompt", {"city": "London"})
    result2 = await client.get_prompt("assistant_prompt", {"query": "help"})

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Raw Protocol Access

For complete control, use get_prompt_mcp() which returns the full MCP protocol object: 
async with client:
    result = await client.get_prompt_mcp("example_prompt", {"arg": "value"})
    # result -> mcp.types.GetPromptResult