Python: Add Foundry Toolbox MCP skills hosted agent sample

python/samples/04-hosting/foundry-hosted-agents/responses/12_foundry_toolbox_mcp_skills/.dockerignore

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+.venv
+__pycache__
+*.pyc
+*.pyo
+*.pyd
+.Python
+.env

python/samples/04-hosting/foundry-hosted-agents/responses/12_foundry_toolbox_mcp_skills/.env.example

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+FOUNDRY_PROJECT_ENDPOINT="..."
+AZURE_AI_MODEL_DEPLOYMENT_NAME="..."
+TOOLBOX_NAME="..."

python/samples/04-hosting/foundry-hosted-agents/responses/12_foundry_toolbox_mcp_skills/Dockerfile

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+FROM python:3.12-slim
+
+RUN apt-get update && apt-get install -y git && rm -rf /var/lib/apt/lists/*
+
+WORKDIR /app
+
+COPY . user_agent/
+WORKDIR /app/user_agent
+
+RUN if [ -f requirements.txt ]; then \
+        pip install -r requirements.txt; \
+    else \
+        echo "No requirements.txt found"; \
+    fi
+
+EXPOSE 8088
+
+CMD ["python", "main.py"]

python/samples/04-hosting/foundry-hosted-agents/responses/12_foundry_toolbox_mcp_skills/README.md

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+# What this sample demonstrates
+
+An [Agent Framework](https://github.com/microsoft/agent-framework) agent that discovers **MCP-based skills from a Foundry Toolbox** and makes them available via `SkillsProvider(MCPSkillsSource(...))`, hosted using the **Responses protocol**.
+
+The `SkillsProvider` is attached to the agent as a context provider and implements the [Agent Skills](https://agentskills.io/) progressive-disclosure pattern. When the agent is prompted, it discovers available skills in the Foundry Toolbox via the provider:
+
+1. **Advertise** — skill names and descriptions are injected into the system prompt so the agent knows what is available.
+2. **Load** — when the agent decides a skill is relevant, it retrieves the full skill body with detailed instructions via the provider.
+3. **Read resources** — if a skill includes supplementary content (reference documents, assets), the agent reads them on demand via the provider.
+
+This way the full skill body and resources are only loaded when the agent actually needs them, reducing token usage.
+
+## How It Works
+
+### Model Integration
+
+The agent uses `FoundryChatClient` from the Agent Framework to create an OpenAI-compatible Responses client. It connects to the toolbox's MCP endpoint via the `mcp` library's `streamable_http_client`, discovers skills served by the toolbox through `MCPSkillsSource`, and injects them as a context provider via `SkillsProvider`. The toolbox endpoint URL is derived from `FOUNDRY_PROJECT_ENDPOINT` and `TOOLBOX_NAME`.
+
+See [main.py](main.py) for the full implementation.
+
+### Agent Hosting
+
+The agent is hosted using the [Agent Framework](https://github.com/microsoft/agent-framework) with the `ResponsesHostServer`, which provisions a REST API endpoint compatible with the OpenAI Responses protocol.
+
+## Prerequisites
+
+- Python 3.12+
+- An Azure AI Foundry project with a deployed model (e.g., `gpt-5`)
+- A Foundry Toolbox with skills attached (see below)
+- Azure CLI logged in (`az login`)
+
+## Setting up a Foundry Toolbox with skills
+
+This sample requires a Foundry Toolbox that has skills attached to it. Skills are `SKILL.md` files you author once, store centrally in Foundry through the versioned Skills API, and attach to a toolbox so any MCP client can discover and load them.
+
+1. **Author a skill** — Create a `SKILL.md` file following the [Agent Skills](https://agentskills.io/) specification format (YAML front matter with `name` and `description`, plus Markdown body).
+2. **Create the skill in Foundry** — Upload the skill via the Skills REST API, Python SDK, or `azd ai skill create`. See [Use skills with Microsoft Foundry agents](https://learn.microsoft.com/en-us/azure/foundry/agents/how-to/tools/skills).
+3. **Attach the skill to a toolbox** — Add a skill reference to a toolbox version so MCP clients can discover it. See [Attach skills to a toolbox](https://learn.microsoft.com/en-us/azure/foundry/agents/how-to/tools/toolbox#attach-skills-to-a-toolbox).
+
+When the agent connects to the toolbox MCP endpoint, skills are advertised through a well-known `skill://index.json` discovery resource. The `MCPSkillsSource` in this sample reads `skill://index.json` the first time the agent runs to discover all attached skills, then fetches each `SKILL.md` body on demand via `resources/read`.
+
+## Running the Agent Host
+
+Follow the instructions in the [Running the Agent Host Locally](../../README.md#running-the-agent-host-locally) section of the README in the parent directory to run the agent host.
+
+An extra environment variable must be set to point to the toolbox name:
+
+```bash
+export TOOLBOX_NAME="my-toolbox"
+```
+
+Or in PowerShell:
+
+```powershell
+$env:TOOLBOX_NAME="my-toolbox"
+```
+
+You can also place these in a `.env` file next to `main.py` — see [`.env.example`](.env.example).
+
+## Interacting with the agent
+
+> Depending on how you run the agent host, you can invoke the agent using `curl` (`Invoke-WebRequest` in PowerShell) or `azd`. Please refer to the [parent README](../../README.md) for more details. Use this README for sample queries you can send to the agent.
+
+Send a POST request to the server with a JSON body containing an `"input"` field to interact with the agent. For example:
+
+```bash
+curl -X POST http://localhost:8088/responses -H "Content-Type: application/json" -d '{"input": "What skills do you have available?"}'
+```
+
+## Deploying the Agent to Foundry
+
+To host the agent on Foundry, follow the instructions in the [Deploying the Agent to Foundry](../../README.md#deploying-the-agent-to-foundry) section of the README in the parent directory.

python/samples/04-hosting/foundry-hosted-agents/responses/12_foundry_toolbox_mcp_skills/agent.manifest.yaml

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+name: hosted-toolbox-mcp-skills
+displayName: "Hosted Toolbox MCP Skills Agent"
+
+description: >
+  A hosted agent that discovers MCP-based skills from a Foundry Toolbox
+  and makes them available to the agent via the agent skills provider.
+
+metadata:
+  tags:
+    - AI Agent Hosting
+    - Azure AI AgentServer
+    - Responses Protocol
+    - Agent Framework
+    - MCP
+    - Model Context Protocol
+    - Agent Skills
+    - Foundry Toolbox
+    - Foundry Toolbox Skills
+
+template:
+  name: hosted-toolbox-mcp-skills
+  kind: hosted
+  protocols:
+    - protocol: responses
+      version: 1.0.0
+  resources:
+    cpu: "0.25"
+    memory: 0.5Gi
+  environment_variables:
+    - name: AZURE_AI_MODEL_DEPLOYMENT_NAME
+      value: "{{AZURE_AI_MODEL_DEPLOYMENT_NAME}}"
+    - name: TOOLBOX_NAME
+      value: "{{TOOLBOX_NAME}}"
+parameters:
+  properties:
+    - name: TOOLBOX_NAME
+      secret: false
+      description: Name of the Foundry Toolbox to connect to for MCP skill discovery
+resources:
+  - kind: model
+    id: gpt-5
+    name: AZURE_AI_MODEL_DEPLOYMENT_NAME

python/samples/04-hosting/foundry-hosted-agents/responses/12_foundry_toolbox_mcp_skills/agent.yaml

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+# yaml-language-server: $schema=https://github.com/microsoft/AgentSchema/refs/heads/main/schemas/v1.0/ContainerAgent.yaml
+kind: hosted
+name: hosted-toolbox-mcp-skills
+protocols:
+  - protocol: responses
+    version: 1.0.0
+resources:
+  cpu: "0.25"
+  memory: 0.5Gi
+environment_variables:
+  - name: AZURE_AI_MODEL_DEPLOYMENT_NAME
+    value: ${AZURE_AI_MODEL_DEPLOYMENT_NAME}
+  - name: TOOLBOX_NAME
+    value: ${TOOLBOX_NAME}

python/samples/04-hosting/foundry-hosted-agents/responses/12_foundry_toolbox_mcp_skills/main.py

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+# Copyright (c) Microsoft. All rights reserved.
+
+import asyncio
+import os
+from collections.abc import Callable, Generator
+
+import httpx
+from agent_framework import Agent, MCPSkillsSource, SkillsProvider
+from agent_framework.foundry import FoundryChatClient
+from agent_framework_foundry_hosting import ResponsesHostServer
+from azure.identity import DefaultAzureCredential, get_bearer_token_provider
+from dotenv import load_dotenv
+from mcp.client.session import ClientSession
+from mcp.client.streamable_http import streamable_http_client
+
+# Load environment variables from .env file
+load_dotenv()
+
+
+class ToolboxAuth(httpx.Auth):
+    """Attach a fresh Foundry bearer token to every request."""
+
+    def __init__(self, token_provider: Callable[[], str]):
+        self._get_token = token_provider
+
+    def auth_flow(self, request: httpx.Request) -> Generator[httpx.Request, httpx.Response, None]:
+        request.headers["Authorization"] = f"Bearer {self._get_token()}"
+        yield request
+
+
+async def main() -> None:
+    project_endpoint = os.environ["FOUNDRY_PROJECT_ENDPOINT"]
+    deployment = os.environ["AZURE_AI_MODEL_DEPLOYMENT_NAME"]
+    toolbox_name = os.environ["TOOLBOX_NAME"]
+
+    # Build the Toolbox MCP URL from the project endpoint and toolbox name.
+    toolbox_mcp_url = f"{project_endpoint.rstrip('/')}/toolboxes/{toolbox_name}/mcp?api-version=v1"
+
+    credential = DefaultAzureCredential()
+
+    # Create a token provider for Foundry bearer auth
+    token_provider = get_bearer_token_provider(credential, "https://ai.azure.com/.default")
+
+    # ── Connect to the Foundry Toolbox MCP endpoint ──────────────────────────
+    # Create an HTTP client that attaches a fresh Foundry bearer token to every
+    # request and advertises the toolbox preview feature flag.
+    async with (
+        httpx.AsyncClient(
+            auth=ToolboxAuth(token_provider),
+            headers={"Foundry-Features": "Toolboxes=V1Preview"},
+            timeout=httpx.Timeout(30.0, read=300.0),
+            follow_redirects=True,
+        ) as http_client,
+        streamable_http_client(
+            url=toolbox_mcp_url,
+            http_client=http_client,
+        ) as (read, write, _),
+        ClientSession(read, write) as session,
+    ):
+        await session.initialize()
+
+        print(f"Connected to Foundry Toolbox '{toolbox_name}' MCP server.")
+
+        # ── Configure MCP-based skills provider ──────────────────────────────
+        # 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))
+
+        # ── Create the agent ─────────────────────────────────────────────────
+        client = FoundryChatClient(
+            project_endpoint=project_endpoint,
+            model=deployment,
+            credential=credential,
+        )
+
+        agent = Agent(
+            client=client,
+            name=os.environ.get("AGENT_NAME", "hosted-toolbox-mcp-skills"),
+            instructions="You are a helpful assistant.",
+            context_providers=[skills_provider],
+            # History will be managed by the hosting infrastructure, thus there
+            # is no need to store history by the service. Learn more at:
+            # https://developers.openai.com/api/reference/resources/responses/methods/create
+            default_options={"store": False},
+        )
+
+        # ── Build and run the host ───────────────────────────────────────────
+        server = ResponsesHostServer(agent)
+        await server.run_async()
+
+
+if __name__ == "__main__":
+    asyncio.run(main())

python/samples/04-hosting/foundry-hosted-agents/responses/12_foundry_toolbox_mcp_skills/requirements.txt

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+agent-framework
+agent-framework-foundry-hosting
+
+mcp>=1.24.0,<2