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Some MCP clients only support tools. They cannot list or get prompts directly because they lack prompt protocol support. The PromptsAsTools transform bridges this gap by generating tools that provide access to your server’s prompts. When you add PromptsAsTools to a server, it creates two tools that clients can call instead of using the prompt protocol:
  • list_prompts returns JSON describing all available prompts and their arguments
  • get_prompt renders a specific prompt with provided arguments
This means any client that can call tools can now access prompts, even if the client has no native prompt support.

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Basic Usage

Pass your FastMCP server to PromptsAsTools when adding the transform. The generated tools route through the server at runtime, which means all server middleware — auth, visibility, rate limiting — applies to prompt operations automatically, exactly as it would for direct prompts/get calls.
PromptsAsTools (and ResourcesAsTools) should be applied to a FastMCP server instance, not a raw Provider. The generated tools call back into the server’s middleware chain at runtime, so they need a server to route through. If you want to expose only a subset of prompts, create a dedicated FastMCP server for those prompts and apply the transform there.
from fastmcp import FastMCP
from fastmcp.server.transforms import PromptsAsTools

mcp = FastMCP("My Server")

@mcp.prompt
def analyze_code(code: str, language: str = "python") -> str:
    """Analyze code for potential issues."""
    return f"Analyze this {language} code:\n{code}"

@mcp.prompt
def explain_concept(concept: str) -> str:
    """Explain a programming concept."""
    return f"Explain: {concept}"

# Add the transform - creates list_prompts and get_prompt tools
mcp.add_transform(PromptsAsTools(mcp))
Clients now see three items: whatever tools you defined directly, plus list_prompts and get_prompt.

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Listing Prompts

The list_prompts tool returns JSON with metadata for each prompt, including its arguments. 
result = await client.call_tool("list_prompts", {})
prompts = json.loads(result.data)
# [
#   {
#     "name": "analyze_code",
#     "description": "Analyze code for potential issues.",
#     "arguments": [
#       {"name": "code", "description": null, "required": true},
#       {"name": "language", "description": null, "required": false}
#     ]
#   },
#   {
#     "name": "explain_concept",
#     "description": "Explain a programming concept.",
#     "arguments": [
#       {"name": "concept", "description": null, "required": true}
#     ]
#   }
#]
Each argument includes:
  • name: The argument name
  • description: Optional description from type hints or docstrings
  • required: Whether the argument must be provided

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Getting Prompts

The get_prompt tool accepts a prompt name and optional arguments dict. It returns the rendered prompt as JSON with a messages array. 
# Prompt with required and optional arguments
result = await client.call_tool(
    "get_prompt",
    {
        "name": "analyze_code",
        "arguments": {
            "code": "x = 1\nprint(x)",
            "language": "python"
        }
    }
)

response = json.loads(result.data)
# {
#   "messages": [
#     {
#       "role": "user",
#       "content": "Analyze this python code:\nx = 1\nprint(x)"
#     }
#   ]
# }
If a prompt has no arguments, you can omit the arguments field or pass an empty dict: 
result = await client.call_tool(
    "get_prompt",
    {"name": "simple_prompt"}
)

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Message Format

Rendered prompts return a messages array following the standard MCP format. Each message includes:
  • role: The message role (“user” or “assistant”)
  • content: The message text content
Multi-message prompts are supported - the array will contain all messages in order.

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Binary Content

Unlike resources, prompts always return text content. There is no binary encoding needed.