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Address PR review comments: rename to MCP* convention, fix error handling and samples
- Rename McpSkill/McpSkillResource/McpSkillsSource to MCPSkill/MCPSkillResource/MCPSkillsSource - Add data-URI prefix stripping for blob resource decoding - Let non-McpError exceptions propagate from get_resource() - Fix contradictory test comment - Use interactive input() in mcp_based_skill sample - Remove misleading sample output block Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
This commit is contained in:
SergeyMenshykh
@@ -27,7 +27,7 @@ This folder contains Azure AI Foundry and Foundry Local samples for Agent Framew
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| [`foundry_chat_client_with_local_mcp.py`](foundry_chat_client_with_local_mcp.py) | Foundry Chat Client with local MCP |
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| [`foundry_chat_client_with_session.py`](foundry_chat_client_with_session.py) | Foundry Chat Client with session management |
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| [`foundry_chat_client_with_toolbox.py`](foundry_chat_client_with_toolbox.py) | Foundry Chat Client connected to a toolbox via its MCP endpoint using `MCPStreamableHTTPTool` |
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| [`foundry_chat_client_with_toolbox_skills.py`](foundry_chat_client_with_toolbox_skills.py) | Foundry Chat Client that discovers MCP-based skills from a Foundry Toolbox endpoint via `McpSkillsSource` (uses an Azure AD bearer token and the toolbox preview header) |
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| [`foundry_chat_client_with_toolbox_skills.py`](foundry_chat_client_with_toolbox_skills.py) | Foundry Chat Client that discovers MCP-based skills from a Foundry Toolbox endpoint via `MCPSkillsSource` (uses an Azure AD bearer token and the toolbox preview header) |
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## FoundryLocalClient Samples
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+4
-4
@@ -5,7 +5,7 @@ import os
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from collections.abc import Generator
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import httpx
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from agent_framework import Agent, McpSkillsSource, SkillsProvider
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from agent_framework import Agent, MCPSkillsSource, SkillsProvider
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from agent_framework.foundry import FoundryChatClient
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from azure.core.credentials import TokenCredential
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from azure.identity import DefaultAzureCredential, get_bearer_token_provider
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@@ -20,7 +20,7 @@ load_dotenv()
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Foundry Chat Client with Toolbox-Hosted Skills
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Discover Agent Skills served by a Microsoft Foundry Toolbox MCP endpoint
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and inject them into a ``FoundryChatClient`` agent via ``McpSkillsSource``.
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and inject them into a ``FoundryChatClient`` agent via ``MCPSkillsSource``.
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The toolbox's discovery document (``skill://index.json``) is read once at
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startup; SKILL.md bodies are fetched on demand as the agent uses them.
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@@ -72,11 +72,11 @@ async def main() -> None:
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await session.initialize()
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# Discover skills served by the toolbox and inject them as a context provider.
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skills_provider = SkillsProvider(McpSkillsSource(client=session))
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skills_provider = SkillsProvider(MCPSkillsSource(client=session))
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async with Agent(
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client=FoundryChatClient(credential=credential),
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name="ToolboxMcpSkillsAgent",
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name="ToolboxMCPSkillsAgent",
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instructions="You are a helpful assistant. Use available skills to answer the user.",
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context_providers=[skills_provider],
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) as agent:
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@@ -12,7 +12,7 @@ Start with file-based or code-defined skills, then explore combining them and ad
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| [**code_defined_skill**](code_defined_skill/) | Define skills entirely in Python code using `Skill`, `@skill.resource`, and `@skill.script` decorators. Uses a code-defined unit-converter skill. |
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| [**class_based_skill**](class_based_skill/) | Define skills as Python classes using `ClassSkill` with `@ClassSkill.resource` and `@ClassSkill.script` decorators for auto-discovery. Uses a class-based unit-converter skill. |
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| [**mixed_skills**](mixed_skills/) | Combine code-defined, class-based, and file-based skills in a single agent. Uses a code-defined volume-converter, a class-based temperature-converter, and a file-based unit-converter. |
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| [**mcp_based_skill**](mcp_based_skill/) | Discover skills served over the [Model Context Protocol (MCP)](https://modelcontextprotocol.io) via `McpSkillsSource`. Connects to a remote MCP server that exposes skills as `skill://...` resources following the SEP-2640 convention. |
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| [**mcp_based_skill**](mcp_based_skill/) | Discover skills served over the [Model Context Protocol (MCP)](https://modelcontextprotocol.io) via `MCPSkillsSource`. Connects to a remote MCP server that exposes skills as `skill://...` resources following the SEP-2640 convention. |
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| [**script_approval**](script_approval/) | Require human-in-the-loop approval before executing skill scripts |
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## Key Concepts
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@@ -6,7 +6,7 @@ This sample demonstrates how to discover **Agent Skills served over MCP** with a
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- Connecting to a remote MCP server (over streamable HTTP) that exposes skill
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resources following the SEP-2640 convention.
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- Building a `SkillsProvider` from an `McpSkillsSource`, which reads
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- Building a `SkillsProvider` from an `MCPSkillsSource`, which reads
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`skill://index.json` (SEP-2640 canonical discovery) and constructs skills from
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the index entries.
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- The progressive disclosure pattern across MCP: advertise → load → read
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@@ -7,7 +7,7 @@ import os
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# using the sample's Skills APIs.
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# import warnings
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# warnings.filterwarnings("ignore", message=r"\[SKILLS\].*", category=FutureWarning)
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from agent_framework import Agent, McpSkillsSource, SkillsProvider
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from agent_framework import Agent, MCPSkillsSource, SkillsProvider
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from agent_framework.foundry import FoundryChatClient
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from azure.identity import AzureCliCredential
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from dotenv import load_dotenv
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@@ -18,7 +18,7 @@ from mcp.client.streamable_http import streamable_http_client
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MCP-Based Agent Skills
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This sample demonstrates how to discover Agent Skills served over the
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Model Context Protocol (MCP) using :class:`McpSkillsSource`.
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Model Context Protocol (MCP) using :class:`MCPSkillsSource`.
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The sample connects to a remote MCP server that exposes skill resources
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under the ``skill://`` URI scheme:
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@@ -47,10 +47,10 @@ async def main() -> None:
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await session.initialize()
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# 2. Build a SkillsProvider that discovers skills over MCP.
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# McpSkillsSource reads skill://index.json and creates one
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# McpSkill per skill-md entry; SKILL.md bodies are fetched
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# MCPSkillsSource reads skill://index.json and creates one
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# MCPSkill per skill-md entry; SKILL.md bodies are fetched
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# on demand via resources/read.
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skills_provider = SkillsProvider(McpSkillsSource(client=session))
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skills_provider = SkillsProvider(MCPSkillsSource(client=session))
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# 3. Run the agent.
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client = FoundryChatClient(
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@@ -64,25 +64,13 @@ async def main() -> None:
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instructions="You are a helpful assistant. Use available skills to answer the user.",
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context_providers=[skills_provider],
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) as agent:
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response = await agent.run(
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"What skills do you have?"
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)
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query = input("User: ").strip() # noqa: ASYNC250
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if not query:
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return
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response = await agent.run(query)
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print(f"Agent: {response}\n")
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if __name__ == "__main__":
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asyncio.run(main())
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"""
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Sample output:
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Discovering MCP-based skills
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------------------------------------------------------------
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Agent: Here are your conversions:
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1. **26.2 miles -> 42.16 km** (a marathon distance)
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2. **75 kg -> 165.35 lbs**
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Conversion factors used: miles * 1.60934 and kilograms * 2.20462.
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"""
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