Implement API to send chat history for OpenAI orchestrator

CLAUDE.md

@@ -168,12 +168,45 @@ Place it before imports with one blank line after.
 
 ### Python Conventions
 
-- Type hints preferred (Pydantic models heavily used)
+- Type hints required on all function parameters and return types
 - Async/await patterns for I/O operations
 - Use explicit `None` checks: `if x is not None:` not `if x:`
 - Local imports should be moved to top of file
 - Return defensive copies of mutable data to protect singletons
 
+### Type Hints - NEVER Use `Any`
+
+**CRITICAL: Never use `typing.Any` in this codebase.** Using `Any` defeats the purpose of type checking and can hide bugs. Instead:
+
+1. **Use actual types from external SDKs** - When integrating with external libraries (OpenAI, LangChain, etc.), import and use their actual types:
+   ```python
+   from agents.memory import Session
+   from agents.items import TResponseInputItem
+
+   async def send_chat_history(self, session: Session) -> OperationResult:
+       ...
+   ```
+
+2. **Use `Union` for known possible types**:
+   ```python
+   from typing import Union
+   MessageType = Union[UserMessage, AssistantMessage, SystemMessage, Dict[str, object]]
+   ```
+
+3. **Use `object` for truly unknown types** that you only pass through:
+   ```python
+   def log_item(item: object) -> None: ...
+   ```
+
+4. **Use `Protocol` only as a last resort** - If external types cannot be found or imported, define a Protocol. However, **confirm with the developer first** before proceeding with this approach, as it may indicate a missing dependency or incorrect understanding of the external API.
+
+**Why this matters:**
+- `Any` disables all type checking for that variable
+- Bugs that type checkers would catch go unnoticed
+- Code readability suffers - developers don't know what types to expect
+- Using actual SDK types provides better IDE support and ensures compatibility
+- This applies to both production code AND test files
+
 ## CI/CD
 
 The `.github/workflows/ci.yml` pipeline:

docs/design.md

@@ -227,9 +227,51 @@ Framework-specific adapters for MCP tool integration:
 |---------|---------|------------|
 | `extensions-agentframework` | Adapt MCP tools to Microsoft Agents SDK | [design.md](../libraries/microsoft-agents-a365-tooling-extensions-agentframework/docs/design.md) |
 | `extensions-azureaifoundry` | Azure AI Foundry tool integration | [design.md](../libraries/microsoft-agents-a365-tooling-extensions-azureaifoundry/docs/design.md) |
-| `extensions-openai` | OpenAI function calling integration | [design.md](../libraries/microsoft-agents-a365-tooling-extensions-openai/docs/design.md) |
+| `extensions-openai` | OpenAI function calling integration and chat history | [design.md](../libraries/microsoft-agents-a365-tooling-extensions-openai/docs/design.md) |
 | `extensions-semantickernel` | Semantic Kernel plugin integration | [design.md](../libraries/microsoft-agents-a365-tooling-extensions-semantickernel/docs/design.md) |
 
+#### OpenAI Extension: Chat History API
+
+The OpenAI tooling extension provides methods to send chat history to the MCP platform for real-time threat protection:
+
+**Key Classes:**
+
+| Class | Purpose |
+|-------|---------|
+| `McpToolRegistrationService` | MCP tool registration and chat history management |
+
+**Methods:**
+
+| Method | Purpose |
+|--------|---------|
+| `send_chat_history(turn_context, session, limit, options)` | Extract messages from OpenAI Session and send to MCP platform |
+| `send_chat_history_messages(turn_context, messages, options)` | Send a list of OpenAI TResponseInputItem messages to MCP platform |
+
+**Usage Example:**
+
+```python
+from agents import Agent, Runner
+from microsoft_agents_a365.tooling.extensions.openai import McpToolRegistrationService
+
+service = McpToolRegistrationService()
+agent = Agent(name="my-agent", model="gpt-4")
+
+# In your agent handler:
+async with Runner.run(agent, messages) as result:
+    session = result.session
+
+    # Option 1: Send from Session object
+    op_result = await service.send_chat_history(turn_context, session)
+
+    # Option 2: Send from message list
+    op_result = await service.send_chat_history_messages(turn_context, messages)
+
+    if op_result.succeeded:
+        print("Chat history sent successfully")
+```
+
+The methods convert OpenAI message types to `ChatHistoryMessage` format and delegate to the core `McpToolServerConfigurationService.send_chat_history()` method.
+
 ### 6. Notifications (`microsoft-agents-a365-notifications`)
 
 > **Detailed documentation**: [libraries/microsoft-agents-a365-notifications/docs/design.md](../libraries/microsoft-agents-a365-notifications/docs/design.md)

libraries/microsoft-agents-a365-tooling-extensions-openai/microsoft_agents_a365/tooling/extensions/openai/__init__.py

@@ -2,10 +2,21 @@
 # Licensed under the MIT License.
 
 """
-OpenAI extensions for Microsoft Agent 365 Tooling SDK
+OpenAI extensions for Microsoft Agent 365 Tooling SDK.
 
 Tooling and utilities specifically for OpenAI framework integration.
-Provides OpenAI-specific helper utilities.
+Provides OpenAI-specific helper utilities including:
+- McpToolRegistrationService: Service for MCP tool registration and chat history management
+
+For type hints, use the types directly from the OpenAI Agents SDK:
+- agents.memory.Session: Protocol for session objects
+- agents.items.TResponseInputItem: Type for input message items
 """
 
+from .mcp_tool_registration_service import McpToolRegistrationService
+
 __version__ = "1.0.0"
+
+__all__ = [
+    "McpToolRegistrationService",
+]