chore: import upstream snapshot with attribution

examples/stories/sampling/client.py

@@ -0,0 +1,30 @@
+"""Supply a canned sampling_callback and assert its text round-trips through the tool."""
+
+from mcp_types import CreateMessageRequestParams, CreateMessageResult, TextContent
+
+from mcp.client import Client, ClientRequestContext
+from stories._harness import Target, run_client
+
+
+async def on_sample(context: ClientRequestContext, params: CreateMessageRequestParams) -> CreateMessageResult:
+    # A real host would call its LLM provider here; the example returns a deterministic
+    # canned answer so the round-trip is assertable.
+    return CreateMessageResult(
+        role="assistant",
+        content=TextContent(text="[canned summary]"),
+        model="stub-model",
+        stop_reason="endTurn",
+    )
+
+
+async def main(target: Target, *, mode: str = "auto") -> None:
+    async with Client(target, mode=mode, sampling_callback=on_sample) as client:
+        result = await client.call_tool("summarize", {"text": "hello world"})
+
+        assert not result.is_error, result
+        assert isinstance(result.content[0], TextContent)
+        assert result.content[0].text == "[canned summary]", result.content[0].text
+
+
+if __name__ == "__main__":
+    run_client(main)