chore: import upstream snapshot with attribution

03-GettingStarted/03-llm-client/solution/python/README.md

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+# Running this sample
+
+You're recommended to install `uv` but it's not a must, see [instructions](https://docs.astral.sh/uv/#highlights)
+
+## -0- Create a virtual environment
+
+```bash
+python -m venv venv
+```
+
+## -1- Activate the virtual environment
+
+```bash
+venv\Scrips\activate
+```
+
+## -2- Install the dependencies
+
+```bash
+pip install "mcp[cli]"
+pip install openai
+pip install azure-ai-inference
+```
+
+## -3- Run the sample
+
+
+```bash
+python client.py
+```
+
+You should see an output similar to:
+
+```text
+LISTING RESOURCES
+Resource:  ('meta', None)
+Resource:  ('nextCursor', None)
+Resource:  ('resources', [])
+                    INFO     Processing request of type ListToolsRequest                                                                               server.py:534
+LISTING TOOLS
+Tool:  add
+Tool {'a': {'title': 'A', 'type': 'integer'}, 'b': {'title': 'B', 'type': 'integer'}}
+CALLING LLM
+TOOL:  {'function': {'arguments': '{"a":2,"b":20}', 'name': 'add'}, 'id': 'call_BCbyoCcMgq0jDwR8AuAF9QY3', 'type': 'function'}
+[05/08/25 21:04:55] INFO     Processing request of type CallToolRequest                                                                                server.py:534
+TOOLS result:  [TextContent(type='text', text='22', annotations=None)]
+```

03-GettingStarted/03-llm-client/solution/python/client.py

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+from mcp import ClientSession, StdioServerParameters, types
+from mcp.client.stdio import stdio_client
+
+# llm
+import os
+from azure.ai.inference import ChatCompletionsClient
+from azure.ai.inference.models import SystemMessage, UserMessage
+from azure.core.credentials import AzureKeyCredential
+import json
+
+# Create server parameters for stdio connection
+server_params = StdioServerParameters(
+    command="mcp",  # Executable
+    args=["run", "server.py"],  # Optional command line arguments
+    env=None,  # Optional environment variables
+)
+
+def call_llm(prompt, functions):
+    token = os.environ["GITHUB_TOKEN"]
+    endpoint = "https://models.inference.ai.azure.com"
+
+    model_name = "gpt-4o"
+
+    client = ChatCompletionsClient(
+        endpoint=endpoint,
+        credential=AzureKeyCredential(token),
+    )
+
+    print("CALLING LLM")
+    response = client.complete(
+        messages=[
+            {
+            "role": "system",
+            "content": "You are a helpful assistant.",
+            },
+            {
+            "role": "user",
+            "content": prompt,
+            },
+        ],
+        model=model_name,
+        tools = functions,
+        # Optional parameters
+        temperature=1.,
+        max_tokens=1000,
+        top_p=1.    
+    )
+
+    response_message = response.choices[0].message
+    
+    functions_to_call = []
+
+    if response_message.tool_calls:
+        for tool_call in response_message.tool_calls:
+            print("TOOL: ", tool_call)
+            name = tool_call.function.name
+            args = json.loads(tool_call.function.arguments)
+            functions_to_call.append({ "name": name, "args": args })
+
+    return functions_to_call
+
+def convert_to_llm_tool(tool):
+    tool_schema = {
+        "type": "function",
+        "function": {
+            "name": tool.name,
+            "description": tool.description,
+            "type": "function",
+            "parameters": {
+                "type": "object",
+                "properties": tool.inputSchema["properties"]
+            }
+        }
+    }
+
+    return tool_schema
+
+async def run():
+    async with stdio_client(server_params) as (read, write):
+        async with ClientSession(
+            read, write
+        ) as session:
+            # Initialize the connection
+            await session.initialize()
+
+            # List available resources
+            resources = await session.list_resources()
+            print("LISTING RESOURCES")
+            for resource in resources:
+                print("Resource: ", resource)
+
+            # List available tools
+            tools = await session.list_tools()
+            print("LISTING TOOLS")
+
+            functions = []
+
+            for tool in tools.tools:
+                print("Tool: ", tool.name)
+                print("Tool", tool.inputSchema["properties"])
+                functions.append(convert_to_llm_tool(tool))
+            
+            prompt = "Add 2 to 20"
+
+            # ask LLM what tools to all, if any
+            functions_to_call = call_llm(prompt, functions)
+
+            # call suggested functions
+            for f in functions_to_call:
+                result = await session.call_tool(f["name"], arguments=f["args"])
+                print("TOOLS result: ", result.content)
+
+
+if __name__ == "__main__":
+    import asyncio
+
+    asyncio.run(run())
+
+
+

03-GettingStarted/03-llm-client/solution/python/server.py

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+# server.py
+from mcp.server.fastmcp import FastMCP
+
+# Create an MCP server
+mcp = FastMCP("Demo")
+
+
+# Add an addition tool
+@mcp.tool()
+def add(a: int, b: int) -> int:
+    """Add two numbers"""
+    return a + b
+
+
+# Add a dynamic greeting resource
+@mcp.resource("greeting://{name}")
+def get_greeting(name: str) -> str:
+    """Get a personalized greeting"""
+    return f"Hello, {name}!"
+
+