---
title: prompt
sidebarTitle: prompt
---
# `fastmcp.prompts.prompt`
Base classes for FastMCP prompts.
## Classes
### `Message`
Wrapper for prompt message with auto-serialization.
Accepts any content - strings pass through, other types
(dict, list, BaseModel) are JSON-serialized to text.
**Methods:**
#### `to_mcp_prompt_message`
```python
to_mcp_prompt_message(self) -> PromptMessage
```
Convert to MCP PromptMessage.
### `PromptArgument`
An argument that can be passed to a prompt.
### `PromptResult`
Canonical result type for prompt rendering.
Provides explicit control over prompt responses: multiple messages,
roles, and metadata at both the message and result level.
**Methods:**
#### `to_mcp_prompt_result`
```python
to_mcp_prompt_result(self) -> GetPromptResult
```
Convert to MCP GetPromptResult.
### `Prompt`
A prompt template that can be rendered with parameters.
**Methods:**
#### `to_mcp_prompt`
```python
to_mcp_prompt(self, **overrides: Any) -> SDKPrompt
```
Convert the prompt to an MCP prompt.
#### `from_function`
```python
from_function(fn: Callable[..., Any], name: str | None = None, title: str | None = None, description: str | None = None, icons: list[Icon] | None = None, tags: set[str] | None = None, meta: dict[str, Any] | None = None, task: bool | TaskConfig | None = None) -> FunctionPrompt
```
Create a Prompt from a function.
The function can return:
- str: wrapped as single user Message
- list\[Message | str]: converted to list\[Message]
- PromptResult: used directly
#### `render`
```python
render(self, arguments: dict[str, Any] | None = None) -> str | list[Message | str] | PromptResult
```
Render the prompt with arguments.
Subclasses must implement this method. Return one of:
- str: Wrapped as single user Message
- list\[Message | str]: Converted to list\[Message]
- PromptResult: Used directly
#### `convert_result`
```python
convert_result(self, raw_value: Any) -> PromptResult
```
Convert a raw return value to PromptResult.
**Raises:**
- `TypeError`: for unsupported types
#### `register_with_docket`
```python
register_with_docket(self, docket: Docket) -> None
```
Register this prompt with docket for background execution.
#### `add_to_docket`
```python
add_to_docket(self, docket: Docket, arguments: dict[str, Any] | None, **kwargs: Any) -> Execution
```
Schedule this prompt for background execution via docket.
**Args:**
- `docket`: The Docket instance
- `arguments`: Prompt arguments
- `fn_key`: Function lookup key in Docket registry (defaults to self.key)
- `task_key`: Redis storage key for the result
- `**kwargs`: Additional kwargs passed to docket.add()
### `FunctionPrompt`
A prompt that is a function.
**Methods:**
#### `from_function`
```python
from_function(cls, fn: Callable[..., Any], name: str | None = None, title: str | None = None, description: str | None = None, icons: list[Icon] | None = None, tags: set[str] | None = None, meta: dict[str, Any] | None = None, task: bool | TaskConfig | None = None) -> FunctionPrompt
```
Create a Prompt from a function.
The function can return:
- str: wrapped as single user Message
- list\[Message | str]: converted to list\[Message]
- PromptResult: used directly
#### `render`
```python
render(self, arguments: dict[str, Any] | None = None) -> PromptResult
```
Render the prompt with arguments.
#### `register_with_docket`
```python
register_with_docket(self, docket: Docket) -> None
```
Register this prompt with docket for background execution.
FunctionPrompt registers the underlying function, which has the user's
Depends parameters for docket to resolve.
#### `add_to_docket`
```python
add_to_docket(self, docket: Docket, arguments: dict[str, Any] | None, **kwargs: Any) -> Execution
```
Schedule this prompt for background execution via docket.
FunctionPrompt splats the arguments dict since .fn expects **kwargs.
**Args:**
- `docket`: The Docket instance
- `arguments`: Prompt arguments
- `fn_key`: Function lookup key in Docket registry (defaults to self.key)
- `task_key`: Redis storage key for the result
- `**kwargs`: Additional kwargs passed to docket.add()