chore: import upstream snapshot with attribution

python/samples/02-agents/skills/mcp_based_skill/README.md

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+# MCP-Based Agent Skills Sample
+
+This sample demonstrates how to discover **Agent Skills served over MCP** with an `Agent`.
+
+## What it demonstrates
+
+- Connecting to a remote MCP server (over streamable HTTP) that exposes skill
+  resources following the SEP-2640 convention.
+- Building a `SkillsProvider` from an `MCPSkillsSource`, which reads
+  `skill://index.json` (SEP-2640 canonical discovery) and constructs skills from
+  the index entries.
+- The progressive disclosure pattern across MCP: advertise → load → read
+  resources, exactly as for filesystem-backed skills.
+
+## Running the Sample
+
+### Prerequisites
+
+- Python 3.10+
+- An [Azure AI Foundry](https://ai.azure.com/) project with a deployed model
+- Azure CLI authentication (`az login`)
+- A running MCP server that hosts SEP-2640 skill resources (see "Providing
+  an MCP server" below)
+
+### Setup
+
+Set the following environment variables (in a `.env` file or your shell):
+
+```powershell
+$env:FOUNDRY_PROJECT_ENDPOINT="https://your-endpoint.services.ai.azure.com/api/projects/your-project"
+$env:FOUNDRY_MODEL="gpt-4o-mini"
+$env:MCP_SKILLS_SERVER_URL="https://your-mcp-server.example.com/mcp"
+```
+
+### Run
+
+```powershell
+python mcp_based_skill.py
+```
+
+## Providing an MCP server
+
+This sample is a **consumer**: it does not host an MCP server itself. To try
+it end-to-end you need an MCP server that exposes the SEP-2640 skill
+resources (`skill://index.json` plus per-skill `SKILL.md`).
+
+- See [`samples/02-agents/mcp/agent_as_mcp_server.py`](../../mcp/agent_as_mcp_server.py)
+  for an example of hosting an MCP server via the Agent Framework.
+- The Model Context Protocol working group maintains reference MCP-skills
+  servers at
+  [`modelcontextprotocol/experimental-ext-skills`](https://github.com/modelcontextprotocol/experimental-ext-skills).
+
+## Security Considerations
+
+Discovering skills over MCP means an *external* MCP server controls what skill content
+(including instructions and, for script-capable skills, the scripts the agent may run)
+reaches the agent. A compromised or untrustworthy server could return adversarial content
+designed to manipulate the agent (indirect prompt injection) or to exfiltrate data through
+skill instructions/scripts. This source is never enabled by default — connecting
+`MCPSkillsSource` to a server is an explicit opt-in. Only connect to MCP servers you have
+vetted and trust, and treat their responses as untrusted input.

python/samples/02-agents/skills/mcp_based_skill/mcp_based_skill.py

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+# Copyright (c) Microsoft. All rights reserved.
+
+import asyncio
+import os
+
+# Uncomment this filter to suppress the experimental MCP Skills warning before
+# using the sample's MCP Skills APIs.
+# import warnings
+# warnings.filterwarnings("ignore", message=r"\[MCP_SKILLS\].*", category=FutureWarning)
+from agent_framework import Agent, MCPSkillsSource, SkillsProvider, ToolApprovalMiddleware
+from agent_framework.foundry import FoundryChatClient
+from azure.identity import AzureCliCredential
+from dotenv import load_dotenv
+from mcp.client.session import ClientSession
+from mcp.client.streamable_http import streamable_http_client
+
+"""
+MCP-Based Agent Skills
+
+This sample demonstrates how to discover Agent Skills served over the
+Model Context Protocol (MCP) using :class:`MCPSkillsSource`.
+
+The sample connects to a remote MCP server that exposes skill resources
+under the ``skill://`` URI scheme:
+
+* ``skill://index.json`` — discovery document listing all skills
+* ``skill://<skill-name>/SKILL.md`` — the skill instructions
+
+To run, set ``MCP_SKILLS_SERVER_URL`` to the streamable HTTP endpoint of an
+MCP server that hosts the skill resources.
+"""
+
+
+async def main() -> None:
+    """Connect to a remote MCP skills server and run the agent."""
+    load_dotenv()
+
+    endpoint = os.environ["FOUNDRY_PROJECT_ENDPOINT"]
+    deployment = os.environ.get("FOUNDRY_MODEL", "gpt-4o-mini")
+    mcp_url = os.environ["MCP_SKILLS_SERVER_URL"]
+
+    print("Discovering MCP-based skills")
+    print("-" * 60)
+
+    # 1. Connect to the MCP server over streamable HTTP.
+    async with streamable_http_client(url=mcp_url) as (read, write, _), ClientSession(read, write) as session:
+        await session.initialize()
+
+        # 2. Build a SkillsProvider that discovers skills over MCP.
+        #    MCPSkillsSource reads skill://index.json and creates one
+        #    MCPSkill per skill-md entry; SKILL.md bodies are fetched
+        #    on demand via resources/read.
+        skills_provider = SkillsProvider(MCPSkillsSource(client=session))
+
+        # 3. Run the agent.
+        client = FoundryChatClient(
+            project_endpoint=endpoint,
+            model=deployment,
+            credential=AzureCliCredential(),
+        )
+
+        async with Agent(
+            client=client,
+            instructions="You are a helpful assistant. Use available skills to answer the user.",
+            context_providers=[skills_provider],
+            middleware=[ToolApprovalMiddleware(auto_approval_rules=[SkillsProvider.all_tools_auto_approval_rule])],
+        ) as agent:
+            query = input("User: ").strip()  # noqa: ASYNC250
+            if not query:
+                return
+            session = agent.create_session()
+            response = await agent.run(query, session=session)
+            print(f"Agent: {response}\n")
+
+
+if __name__ == "__main__":
+    asyncio.run(main())