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contributing/samples/mcp/mcp_server_side_sampling/README.md

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+# FastMCP Server-Side Sampling with ADK
+
+This project demonstrates how to use server-side sampling with a `fastmcp` server connected to an ADK `MCPToolset`.
+
+## Description
+
+The setup consists of two main components:
+
+1. **ADK Agent (`agent.py`):** An `LlmAgent` is configured with an `MCPToolset`. This toolset connects to a local `fastmcp` server.
+1. **FastMCP Server (`mcp_server.py`):** A `fastmcp` server that exposes a single tool, `analyze_sentiment`. This server is configured to use its own LLM for sampling, independent of the ADK agent's LLM.
+
+The flow is as follows:
+
+1. The user provides a text prompt to the ADK agent.
+1. The agent decides to use the `analyze_sentiment` tool from the `MCPToolset`.
+1. The tool call is sent to the `mcp_server.py`.
+1. Inside the `analyze_sentiment` tool, `ctx.sample()` is called. This delegates an LLM call to the `fastmcp` server's own sampling handler.
+1. The `mcp_server`'s LLM processes the prompt from `ctx.sample()` and returns the result to the server.
+1. The server processes the LLM response and returns the final sentiment to the agent.
+1. The agent displays the result to the user.
+
+## Steps to Run
+
+### Prerequisites
+
+- Python 3.10+
+- `google-adk` library installed.
+- A configured OpenAI API key.
+
+### 1. Set up the Environment
+
+Clone the project and navigate to the directory. Make sure your `OPENAI_API_KEY` is available as an environment variable.
+
+### 2. Install Dependencies
+
+Install the required Python libraries:
+
+```bash
+pip install fastmcp openai litellm
+```
+
+### 3. Run the Example
+
+Navigate to the `samples` directory and choose this ADK agent:
+
+```bash
+adk web .
+```
+
+The agent will automatically start the FastMCP server in the background.
+
+- **Sample user prompt:** "What is the sentiment of 'I love building things with Python'?"