Skip to main content

​
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 

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 

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.