chore: import upstream snapshot with attribution

contributing/samples/mcp/mcp_server_side_sampling/mcp_server.py

@@ -0,0 +1,81 @@
+# Copyright 2026 Google LLC
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import logging
+import os
+
+from fastmcp import Context
+from fastmcp import FastMCP
+from fastmcp.experimental.sampling.handlers.openai import OpenAISamplingHandler
+from openai import OpenAI
+
+logging.basicConfig(level=logging.INFO)
+API_KEY = os.getenv("OPENAI_API_KEY")
+
+# Set up the server's LLM handler using the OpenAI API.
+# This handler will be used for all sampling requests from tools on this server.
+llm_handler = OpenAISamplingHandler(
+    default_model="gpt-4o",
+    client=OpenAI(
+        api_key=API_KEY,
+    ),
+)
+
+
+# Create the FastMCP Server instance.
+# The `sampling_handler` is configured to use the server's own LLM.
+# `sampling_handler_behavior="always"` ensures the server never delegates
+# sampling back to the ADK agent.
+mcp = FastMCP(
+    name="SentimentAnalysis",
+    sampling_handler=llm_handler,
+    sampling_handler_behavior="always",
+)
+
+
+@mcp.tool
+async def analyze_sentiment(text: str, ctx: Context) -> dict:
+  """Analyzes sentiment by delegating to the server's own LLM."""
+  logging.info("analyze_sentiment tool called with text: %s", text)
+  prompt = f"""Analyze the sentiment of the following text as positive,
+                 negative, or neutral. Just output a single word.
+                 Text to analyze: {text}"""
+
+  # This delegates the LLM call to the server's own sampling handler,
+  # as configured in the FastMCP instance.
+  logging.info("Attempting to call ctx.sample()")
+  try:
+    response = await ctx.sample(prompt)
+    logging.info("ctx.sample() successful. Response: %s", response)
+  except Exception as e:
+    logging.error("ctx.sample() failed: %s", e, exc_info=True)
+    raise
+
+  sentiment = response.text.strip().lower()
+
+  if "positive" in sentiment:
+    result = "positive"
+  elif "negative" in sentiment:
+    result = "negative"
+  else:
+    result = "neutral"
+
+  logging.info("Sentiment analysis result: %s", result)
+  return {"text": text, "sentiment": result}
+
+
+if __name__ == "__main__":
+  print("Starting FastMCP server with tool 'analyze_sentiment'...")
+  # This runs the server process, which the ADK agent will connect to.
+  mcp.run()