> ## Documentation Index
> Fetch the complete documentation index at: https://fastmcp-docs-v3-beta2-feature-tracking.mintlify.site/llms.txt
> Use this file to discover all available pages before exploring further.
# sampling_tool
# `fastmcp.server.sampling.sampling_tool`
SamplingTool for use during LLM sampling requests.
## Classes
### `SamplingTool`
A tool that can be used during LLM sampling.
SamplingTools bundle a tool's schema (name, description, parameters) with
an executor function, enabling servers to execute agentic workflows where
the LLM can request tool calls during sampling.
In most cases, pass functions directly to ctx.sample():
def search(query: str) -> str:
'''Search the web.'''
return web\_search(query)
result = await context.sample(
messages="Find info about Python",
tools=\[search], # Plain functions work directly
)
Create a SamplingTool explicitly when you need custom name/description:
tool = SamplingTool.from\_function(search, name="web\_search")
**Methods:**
#### `run`
```python theme={null}
run(self, arguments: dict[str, Any] | None = None) -> Any
```
Execute the tool with the given arguments.
**Args:**
* `arguments`: Dictionary of arguments to pass to the tool function.
**Returns:**
* The result of executing the tool function.
#### `from_function`
```python theme={null}
from_function(cls, fn: Callable[..., Any]) -> SamplingTool
```
Create a SamplingTool from a function.
The function's signature is analyzed to generate a JSON schema for
the tool's parameters. Type hints are used to determine parameter types.
**Args:**
* `fn`: The function to create a tool from.
* `name`: Optional name override. Defaults to the function's name.
* `description`: Optional description override. Defaults to the function's docstring.
**Returns:**
* A SamplingTool wrapping the function.
**Raises:**
* `ValueError`: If the function is a lambda without a name override.