Initial implementation of invoke mcp tool in python

python/samples/03-workflows/declarative/invoke_mcp_tool/main.py

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+# Copyright (c) Microsoft. All rights reserved.
+
+"""Invoke MCP Tool sample - demonstrates the InvokeMcpTool declarative action.
+
+This sample shows how to:
+  1. Configure a ``WorkflowFactory`` with a ``MCPToolHandler`` so the YAML
+     ``InvokeMcpTool`` action can dispatch real MCP tool calls.
+  2. Invoke a tool on a public unauthenticated MCP server (the Microsoft
+     Learn Docs MCP server at ``https://learn.microsoft.com/api/mcp``,
+     calling ``microsoft_docs_search``).
+  3. Bind the parsed tool result to a workflow variable and mirror it into
+     the conversation via ``conversationId`` so a downstream Foundry agent
+     can answer questions using only that context.
+
+Security note:
+    ``DefaultMCPToolHandler`` connects to whatever MCP server URL the
+    workflow author specifies and performs **no** allowlisting or SSRF
+    guards. For production use, replace it with a custom handler that
+    enforces an allowlist and adds any required authentication headers
+    per server. MCP tool outputs flow back into agent conversations and
+    therefore share the same prompt-injection risk surface as
+    ``HttpRequestAction``: only invoke MCP servers you trust.
+
+Run with:
+    python -m samples.03-workflows.declarative.invoke_mcp_tool.main
+"""
+
+import asyncio
+import os
+from pathlib import Path
+
+from agent_framework import Agent
+from agent_framework.declarative import (
+    DefaultMCPToolHandler,
+    WorkflowFactory,
+)
+from agent_framework.foundry import FoundryChatClient
+from azure.identity import AzureCliCredential
+
+DOCS_AGENT_INSTRUCTIONS = """\
+You answer the user's question about Microsoft technology using ONLY the
+search results already present in the conversation history. If the answer is
+not contained in the conversation, say so plainly rather than guessing. Be
+concise and cite the relevant document title or URL when possible.
+"""
+
+
+async def main() -> None:
+    """Run the invoke MCP tool workflow."""
+    chat_client = FoundryChatClient(
+        project_endpoint=os.environ["FOUNDRY_PROJECT_ENDPOINT"],
+        model=os.environ["FOUNDRY_MODEL"],
+        credential=AzureCliCredential(),
+    )
+
+    # The agent has no tools — it answers using only the search results that
+    # ``InvokeMcpTool`` adds to the conversation.
+    docs_agent = Agent(
+        client=chat_client,
+        name="DocsAgent",
+        instructions=DOCS_AGENT_INSTRUCTIONS,
+    )
+
+    agents = {"DocsAgent": docs_agent}
+
+    # The default MCPToolHandler is sufficient for this sample because the
+    # Microsoft Learn Docs MCP server is public and unauthenticated. For
+    # authenticated servers, supply a ``client_provider`` callback to route
+    # requests through a pre-configured ``httpx.AsyncClient`` carrying the
+    # appropriate credentials, or wrap the handler with one that injects
+    # headers per call.
+    async with DefaultMCPToolHandler() as mcp_handler:
+        factory = WorkflowFactory(
+            agents=agents,
+            mcp_tool_handler=mcp_handler,
+        )
+
+        workflow_path = Path(__file__).parent / "workflow.yaml"
+        workflow = factory.create_workflow_from_yaml_path(workflow_path)
+
+        print("=" * 60)
+        print("Invoke MCP Tool Workflow Demo")
+        print("=" * 60)
+        print()
+        print("Ask one question that can be answered from the Microsoft Learn docs or provide a keyword to search.")
+        print()
+
+        user_input = input("You: ").strip()  # noqa: ASYNC250
+        if not user_input:
+            user_input = "What is the Agent Framework declarative workflow runtime?"
+
+        print("\nAgent: ", end="", flush=True)
+        async for event in workflow.run(user_input, stream=True):
+            if event.type == "output" and isinstance(event.data, str):
+                print(event.data, end="", flush=True)
+        print()
+
+
+if __name__ == "__main__":
+    asyncio.run(main())

python/samples/03-workflows/declarative/invoke_mcp_tool/workflow.yaml

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+#
+# This workflow demonstrates the InvokeMcpTool declarative action.
+#
+# InvokeMcpTool lets a workflow author call a tool exposed by a Model Context
+# Protocol (MCP) server directly from YAML without writing any Python glue.
+# It can:
+#
+#   - dispatch a tool call against an MCP server (with optional auth headers),
+#   - store the parsed tool result in a workflow variable, and
+#   - add the result to the conversation so a downstream agent can answer
+#     questions based on it.
+#
+# This sample calls ``microsoft_docs_search`` on the public Microsoft Learn
+# Docs MCP server (no authentication required) and uses a Foundry agent to
+# answer a single question about Microsoft technology using the search
+# results.
+#
+# Example inputs (Choose one or provide yours):
+#   How do I configure logging in the Agent Framework?
+#   Gpt-5.4-mini
+#
+kind: Workflow
+trigger:
+
+  kind: OnConversationStart
+  id: workflow_invoke_mcp_tool_demo
+  actions:
+
+    # Capture the user's question into a local variable so the MCP tool call
+    # can pass it as an argument.
+    - kind: SetVariable
+      id: capture_query
+      variable: Local.SearchQuery
+      value: =System.LastMessage.Text
+
+    # Invoke microsoft_docs_search on the Microsoft Learn Docs MCP server.
+    # The result is parsed into Local.SearchResults and also added to the
+    # conversation (via conversationId) so the agent below can answer the
+    # user's question based on it.
+    - kind: InvokeMcpTool
+      id: search_docs
+      conversationId: =System.ConversationId
+      serverUrl: https://learn.microsoft.com/api/mcp
+      serverLabel: MicrosoftLearnDocs
+      toolName: microsoft_docs_search
+      arguments:
+        query: =Local.SearchQuery
+      output:
+        autoSend: false
+        result: Local.SearchResults
+
+    # Use the agent to answer the user's question using the conversation
+    # context (which now contains the MCP search results). The user's
+    # original message is already in the conversation as System.LastMessage,
+    # and the executor's input fallback chain extracts its ``Text`` field
+    # automatically when ``input.messages`` is omitted.
+    - kind: InvokeAzureAgent
+      id: answer_question
+      conversationId: =System.ConversationId
+      agent:
+        name: DocsAgent
+      output:
+        autoSend: true
+        messages: Local.AgentResponse