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Prompts in MCP are reusable templates for structured interactions between AI models and servers. They provide predefined interaction patterns with parameters, enabling consistent and efficient communication for common use cases.

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What are Prompts?

Prompts are interaction templates with:
  • User-controlled invocation requiring explicit activation
  • Parameter definitions for customizable inputs
  • Structured formats for consistent interactions
  • Context-aware content that can adapt to different scenarios
Common examples include:
  • Task planning templates (“Plan a vacation”)
  • Code review workflows (“Review this pull request”)
  • Content generation patterns (“Write a blog post about…”)
  • Analysis frameworks (“Analyze market trends for…”)
  • Decision support templates (“Compare options for…”)

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Characteristics of Prompts

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User Control

Prompts are never invoked automatically - they require explicit user activation, ensuring transparency and control over AI interactions.

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Parameter Support

Prompts can accept parameters to customize their behavior and adapt to specific contexts.

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Reusability

Well-designed prompts can be reused across different contexts and conversations.

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Listing Available Prompts

To see what prompts are available from a connected MCP server: 
import asyncio
from mcp_use import MCPClient

async def list_prompts():
    # Initialize client with server configuration
    config = {
        "mcpServers": {
            # Your server definitions here
        }
    }
    client = MCPClient(config)

    # Connect to servers
    await client.create_all_sessions()

    # Get a session for a specific server
    session = client.get_session("my_server")

    # List all available prompts - always returns fresh data
    prompts = await session.list_prompts()

    for prompt in prompts:
        print(f"Prompt: {prompt.name}")
        print(f"Description: {prompt.description}")
        if prompt.arguments:
            print("Arguments:")
            for arg in prompt.arguments:
                print(f"  - {arg.name}: {arg.description}")
        print("---")

# Run the example
asyncio.run(list_prompts())

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Automatic Prompt List Update

When servers send PromptListChangedNotification, it signals that the prompt list has changed. The list_prompts() method always fetches fresh data from the server, ensuring you get up-to-date information. Important: Always use await session.list_prompts() instead of the deprecated session.prompts property to ensure you get fresh data: 
# ✅ Recommended - always returns fresh data
prompts = await session.list_prompts()

# ⚠️ Deprecated - may return stale data
# prompts = session.prompts

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Getting and Using Prompts

Prompts are retrieved using the get_prompt method: 
import asyncio
from mcp_use import MCPClient

async def use_prompt_example():
    config = {
        "mcpServers": {
            # Your server definitions here
        }
    }
    client = MCPClient(config)
    await client.create_all_sessions()

    session = client.get_session("planning_server")

    # Get a prompt with arguments
    result = await session.get_prompt(
        name="plan_vacation",
        arguments={
            "destination": "Japan",
            "duration": "2 weeks",
            "budget": "$5000",
            "interests": ["culture", "food", "nature"]
        }
    )

    # Use the prompt content
    print(f"Prompt description: {result.description}")
    for message in result.messages:
        print(f"Role: {message.role}")
        print(f"Content: {message.content.text}")

asyncio.run(use_prompt_example())

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Prompt Without Arguments

Some prompts don’t require parameters: 
async def simple_prompt_example():
    config = {
        "mcpServers": {
            # Your server definitions here
        }
    }
    client = MCPClient(config)
    await client.create_all_sessions()

    session = client.get_session("content_server")

    # Get a prompt without arguments
    result = await session.get_prompt(name="writing_tips")

    # Display the prompt content
    for message in result.messages:
        print(f"{message.role}: {message.content.text}")

asyncio.run(simple_prompt_example())

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Prompt Structure

Prompts return structured content with messages: 
# Example of working with prompt results
result = await session.get_prompt("code_review", {"language": "python"})

for message in result.messages:
    # Messages have roles (e.g., "user", "assistant", "system")
    role = message.role

    # Content can be text or other formats
    if hasattr(message.content, 'text'):
        text_content = message.content.text
        print(f"{role}: {text_content}")

    # Handle other content types if needed
    if hasattr(message.content, 'image'):
        print(f"{role}: [Image content]")

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Parameter Completion

Many prompts support parameter completion to help users understand required inputs: 
async def explore_prompt_parameters():
    session = client.get_session("my_server")
    prompts = await session.list_prompts()

    for prompt in prompts:
        print(f"Prompt: {prompt.name}")
        if prompt.arguments:
            print("Required parameters:")
            for arg in prompt.arguments:
                required = "required" if arg.required else "optional"
                print(f"  - {arg.name} ({required}): {arg.description}")
        print()

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Dynamic Prompt Generation

Some prompts can generate different content based on context: 
async def dynamic_prompt_example():
    config = {
        "mcpServers": {
            # Your server definitions here
        }
    }
    client = MCPClient(config)
    await client.create_all_sessions()

    session = client.get_session("adaptive_server")

    # Same prompt with different parameters
    contexts = [
        {"domain": "healthcare", "complexity": "beginner"},
        {"domain": "finance", "complexity": "expert"}
    ]

    for context in contexts:
        result = await session.get_prompt("domain_analysis", context)
        print(f"Analysis for {context['domain']}:")
        for message in result.messages:
            print(f"  {message.content.text[:100]}...")
        print()

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Error Handling

Handle potential errors when working with prompts: 
try:
    result = await session.get_prompt("missing_prompt", {"param": "value"})
    for message in result.messages:
        print(message.content.text)
except Exception as e:
    print(f"Failed to get prompt: {e}")

# Check if prompt exists before using
available_prompts = await session.list_prompts()
prompt_names = [p.name for p in available_prompts]

if "my_prompt" in prompt_names:
    result = await session.get_prompt("my_prompt")
else:
    print("Prompt not available")