Merge pull request #709 from dmytrostruk/more-doc-updates

More doc updates

Author: dmytrostruk

agent-framework/tutorials/agents/agent-as-mcp-tool.md

@@ -1,7 +1,6 @@
 ---
 title: Exposing an agent as an MCP tool
 description: Learn how to expose an agent as a tool over the MCP protocol
-zone_pivot_groups: programming-languages
 author: westey-m
 ms.topic: tutorial
 ms.author: westey
@@ -11,8 +10,6 @@ ms.service: semantic-kernel
 
 # Exposing an agent as an MCP tool
 
-::: zone pivot="programming-language-csharp"
-
 This tutorial shows you how to expose an agent as a tool over the Model Context Protocol (MCP), so it can be used by other systems that support MCP tools.
 
 ## Prerequisites
@@ -81,13 +78,6 @@ await builder.Build().RunAsync();
 
 This will start an MCP server that exposes the agent as a tool over the MCP protocol.
 
-::: zone-end
-::: zone pivot="programming-language-python"
-
-Tutorial coming soon.
-
-::: zone-end
-
 ## Next steps
 
 > [!div class="nextstepaction"]

agent-framework/tutorials/agents/function-tools-approvals.md

@@ -1,7 +1,6 @@
 ---
 title: Using function tools with human in the loop approvals
 description: Learn how to use function tools with human in the loop approvals
-zone_pivot_groups: programming-languages
 author: westey-m
 ms.topic: tutorial
 ms.author: westey
@@ -11,7 +10,6 @@ ms.service: semantic-kernel
 
 # Using function tools with human in the loop approvals
 
-::: zone pivot="programming-language-csharp"
 
 This tutorial step shows you how to use function tools that require human approval with an agent, where the agent is built on the Azure OpenAI Chat Completion service.
 
@@ -97,13 +95,6 @@ Console.WriteLine(await agent.RunAsync(approvalMessage, thread));
 
 Whenever you are using function tools with human in the loop approvals, remember to check for `FunctionApprovalRequestContent` instances in the response, after each agent run, until all function calls have been approved or rejected.
 
-::: zone-end
-::: zone pivot="programming-language-python"
-
-Tutorial coming soon.
-
-::: zone-end
-
 ## Next steps
 
 > [!div class="nextstepaction"]

agent-framework/tutorials/workflows/visualization.md

@@ -1,7 +1,6 @@
 ---
 title: Workflow Visualization
 description: Learn how to visualize workflows using the Agent Framework.
-zone_pivot_groups: programming-languages
 author: TaoChenOSU
 ms.topic: tutorial
 ms.author: taochen
@@ -11,13 +10,6 @@ ms.service: semantic-kernel
 
 # Visualizing Workflows
 
-::: zone pivot="programming-language-csharp"
-
-Tutorial coming soon.
-
-::: zone-end
-
-::: zone pivot="programming-language-python"
 
 ## Overview
 
@@ -312,5 +304,3 @@ if os.getenv("CI"):
 ### Running the Example
 
 For the complete working implementation with visualization, see the [Concurrent with Visualization sample](https://github.com/microsoft/agent-framework/blob/main/python/samples/getting_started/workflows/visualization/concurrent_with_visualization.py).
-
-::: zone-end

agent-framework/user-guide/model-context-protocol/using-mcp-tools.md

@@ -129,7 +129,122 @@ The full source code and instructions to run this sample is available [here](htt
 ::: zone-end
 ::: zone pivot="programming-language-python"
 
-Coming soon.
+The Python Agent Framework provides comprehensive support for integrating with Model Context Protocol (MCP) servers through multiple connection types. This allows your agents to access external tools and services seamlessly.
+
+## MCP Tool Types
+
+The Agent Framework supports three types of MCP connections:
+
+### MCPStdioTool - Local MCP Servers
+
+Use `MCPStdioTool` to connect to MCP servers that run as local processes using standard input/output:
+
+```python
+import asyncio
+from agent_framework import ChatAgent, MCPStdioTool
+from agent_framework.openai import OpenAIChatClient
+
+async def local_mcp_example():
+    """Example using a local MCP server via stdio."""
+    async with (
+        MCPStdioTool(
+            name="calculator", 
+            command="uvx", 
+            args=["mcp-server-calculator"]
+        ) as mcp_server,
+        ChatAgent(
+            chat_client=OpenAIChatClient(),
+            name="MathAgent",
+            instructions="You are a helpful math assistant that can solve calculations.",
+        ) as agent,
+    ):
+        result = await agent.run(
+            "What is 15 * 23 + 45?", 
+            tools=mcp_server
+        )
+        print(result)
+
+if __name__ == "__main__":
+    asyncio.run(local_mcp_example())
+```
+
+### MCPStreamableHTTPTool - HTTP/SSE MCP Servers
+
+Use `MCPStreamableHTTPTool` to connect to MCP servers over HTTP with Server-Sent Events:
+
+```python
+import asyncio
+from agent_framework import ChatAgent, MCPStreamableHTTPTool
+from agent_framework.azure import AzureAIAgentClient
+from azure.identity.aio import AzureCliCredential
+
+async def http_mcp_example():
+    """Example using an HTTP-based MCP server."""
+    async with (
+        AzureCliCredential() as credential,
+        MCPStreamableHTTPTool(
+            name="Microsoft Learn MCP",
+            url="https://learn.microsoft.com/api/mcp",
+            headers={"Authorization": "Bearer your-token"},
+        ) as mcp_server,
+        ChatAgent(
+            chat_client=AzureAIAgentClient(async_credential=credential),
+            name="DocsAgent",
+            instructions="You help with Microsoft documentation questions.",
+        ) as agent,
+    ):
+        result = await agent.run(
+            "How to create an Azure storage account using az cli?",
+            tools=mcp_server
+        )
+        print(result)
+
+if __name__ == "__main__":
+    asyncio.run(http_mcp_example())
+```
+
+### MCPWebsocketTool - WebSocket MCP Servers
+
+Use `MCPWebsocketTool` to connect to MCP servers over WebSocket connections:
+
+```python
+import asyncio
+from agent_framework import ChatAgent, MCPWebsocketTool
+from agent_framework.openai import OpenAIChatClient
+
+async def websocket_mcp_example():
+    """Example using a WebSocket-based MCP server."""
+    async with (
+        MCPWebsocketTool(
+            name="realtime-data",
+            url="wss://api.example.com/mcp",
+        ) as mcp_server,
+        ChatAgent(
+            chat_client=OpenAIChatClient(),
+            name="DataAgent",
+            instructions="You provide real-time data insights.",
+        ) as agent,
+    ):
+        result = await agent.run(
+            "What is the current market status?",
+            tools=mcp_server
+        )
+        print(result)
+
+if __name__ == "__main__":
+    asyncio.run(websocket_mcp_example())
+```
+
+## Popular MCP Servers
+
+Common MCP servers you can use with Python Agent Framework:
+
+- **Calculator**: `uvx mcp-server-calculator` - Mathematical computations
+- **Filesystem**: `uvx mcp-server-filesystem` - File system operations  
+- **GitHub**: `npx @modelcontextprotocol/server-github` - GitHub repository access
+- **SQLite**: `uvx mcp-server-sqlite` - Database operations
+
+Each server provides different tools and capabilities that extend your agent's functionality while maintaining the security and standardization benefits of the Model Context Protocol.
 
 ::: zone-end
 

agent-framework/user-guide/model-context-protocol/using-mcp-with-foundry-agents.md

@@ -144,11 +144,107 @@ await persistentAgentsClient.Administration.DeleteAgentAsync(agent.Id);
 ::: zone-end
 ::: zone pivot="programming-language-python"
 
-Coming soon.
+## Python Azure AI Foundry MCP Integration
 
-::: zone-end
+Azure AI Foundry provides seamless integration with Model Context Protocol (MCP) servers through the Python Agent Framework. The service manages the MCP server hosting and execution, eliminating infrastructure management while providing secure, controlled access to external tools.
+
+### Environment Setup
+
+Configure your Azure AI Foundry project credentials through environment variables:
+
+```python
+import os
+from azure.identity.aio import AzureCliCredential
+from agent_framework.azure import AzureAIAgentClient
+
+# Required environment variables
+os.environ["AZURE_AI_PROJECT_ENDPOINT"] = "https://<your-project>.services.ai.azure.com/api/projects/<project-id>"
+os.environ["AZURE_AI_MODEL_DEPLOYMENT_NAME"] = "gpt-4o-mini"  # Optional, defaults to this
+```
 
-To learn more refer to the [Azure AI Foundry Model Context Protocol documentation](/azure/ai-foundry/agents/how-to/tools/model-context-protocol).
+### Basic MCP Integration
+
+Create an Azure AI Foundry agent with hosted MCP tools:
+
+```python
+import asyncio
+from agent_framework import HostedMCPTool
+from agent_framework.azure import AzureAIAgentClient
+from azure.identity.aio import AzureCliCredential
+
+async def basic_foundry_mcp_example():
+    """Basic example of Azure AI Foundry agent with hosted MCP tools."""
+    async with (
+        AzureCliCredential() as credential,
+        AzureAIAgentClient(async_credential=credential) as chat_client,
+    ):
+        # Enable Azure AI observability (optional but recommended)
+        await chat_client.setup_azure_ai_observability()
+        
+        # Create agent with hosted MCP tool
+        agent = chat_client.create_agent(
+            name="MicrosoftLearnAgent", 
+            instructions="You answer questions by searching Microsoft Learn content only.",
+            tools=HostedMCPTool(
+                name="Microsoft Learn MCP",
+                url="https://learn.microsoft.com/api/mcp",
+            ),
+        )
+        
+        # Simple query without approval workflow
+        result = await agent.run(
+            "Please summarize the Azure AI Agent documentation related to MCP tool calling?"
+        )
+        print(result)
+
+if __name__ == "__main__":
+    asyncio.run(basic_foundry_mcp_example())
+```
+
+### Multi-Tool MCP Configuration
+
+Use multiple hosted MCP tools with a single agent:
+
+```python
+async def multi_tool_mcp_example():
+    """Example using multiple hosted MCP tools."""
+    async with (
+        AzureCliCredential() as credential,
+        AzureAIAgentClient(async_credential=credential) as chat_client,
+    ):
+        await chat_client.setup_azure_ai_observability()
+        
+        # Create agent with multiple MCP tools
+        agent = chat_client.create_agent(
+            name="MultiToolAgent",
+            instructions="You can search documentation and access GitHub repositories.",
+            tools=[
+                HostedMCPTool(
+                    name="Microsoft Learn MCP",
+                    url="https://learn.microsoft.com/api/mcp",
+                    approval_mode="never_require",  # Auto-approve documentation searches
+                ),
+                HostedMCPTool(
+                    name="GitHub MCP", 
+                    url="https://api.github.com/mcp",
+                    approval_mode="always_require",  # Require approval for GitHub operations
+                    headers={"Authorization": "Bearer github-token"},
+                ),
+            ],
+        )
+        
+        result = await agent.run(
+            "Find Azure documentation and also check the latest commits in microsoft/semantic-kernel"
+        )
+        print(result)
+
+if __name__ == "__main__":
+    asyncio.run(multi_tool_mcp_example())
+```
+
+The Python Agent Framework provides seamless integration with Azure AI Foundry's hosted MCP capabilities, enabling secure and scalable access to external tools while maintaining the flexibility and control needed for production applications.
+
+::: zone-end
 
 ## Next steps
 

agent-framework/user-guide/workflows/checkpoints.md

@@ -221,12 +221,6 @@ protected override async ValueTask OnCheckpointRestoredAsync(IWorkflowContext co
 
 ::: zone-end
 
-::: zone pivot="programming-language-python"
-
-Coming soon...
-
-::: zone-end
-
 ## Next Steps
 
 - [Learn how to use agents in workflows](./using-agents.md) to build intelligent workflows.

agent-framework/user-guide/workflows/observability.md

@@ -1,7 +1,6 @@
 ---
 title: Microsoft Agent Framework Workflows - Observability
 description: In-depth look at Observability in Microsoft Agent Framework Workflows.
-zone_pivot_groups: programming-languages
 author: TaoChenOSU
 ms.topic: tutorial
 ms.author: taochen
@@ -15,14 +14,6 @@ Observability provides insights into the internal state and behavior of workflow
 
 Aside from the standard [GenAI telemetry](https://opentelemetry.io/docs/specs/semconv/gen-ai/), Agent Framework Workflows emits additional spans, logs, and metrics to provide deeper insights into workflow execution. These observability features help developers understand the flow of messages, the performance of executors, and any errors that may occur.
 
-::: zone pivot="programming-language-csharp"
-
-Coming soon...
-
-::: zone-end
-
-::: zone pivot="programming-language-python"
-
 ## Enable Observability
 
 Observability is enabled framework-wide by setting the `ENABLE_OTEL=true` environment variable or calling `setup_observability()` at the beginning of your application.
@@ -58,8 +49,6 @@ For example:
 
 ![Span Relationships](./resources/images/workflow-trace.png)
 
-::: zone-end
-
 ## Next Steps
 
 - [Learn how to use agents in workflows](./using-agents.md) to build intelligent workflows.