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577f4d1bdf* Fix tool_choice to always require tools when result_type is set * Consolidate sampling examples with rich output * Replace eval() with explicit add/multiply tools
1.3 KiB
1.3 KiB
Sampling Examples
These examples demonstrate FastMCP's sampling API, which allows server tools to request LLM completions from the client.
Prerequisites
pip install fastmcp[anthropic]
export ANTHROPIC_API_KEY=your-key
Or run directly with uv:
uv run examples/sampling/text.py
Examples
Simple Text Sampling (text.py)
Basic sampling flow where a server tool requests an LLM completion:
uv run examples/sampling/text.py
Structured Output (structured_output.py)
Uses result_type to get validated Pydantic models from the LLM:
uv run examples/sampling/structured_output.py
Tool Use (tool_use.py)
Gives the LLM tools to use during sampling (calculator, time, dice):
uv run examples/sampling/tool_use.py
Server Fallback (server_fallback.py)
Configures a fallback sampling handler on the server, enabling sampling even when clients don't support it:
uv run examples/sampling/server_fallback.py
Using OpenAI Instead
To use OpenAI instead of Anthropic, change the handler:
from fastmcp.client.sampling.handlers.openai import OpenAISamplingHandler
handler = OpenAISamplingHandler(default_model="gpt-4o-mini")
And install with pip install fastmcp[openai].