Refactor to use LiteLLM Responses API for automatic MCP tool execution
Major refactoring to properly integrate with LiteLLM's Responses API, which handles MCP tool execution automatically instead of requiring manual tool call loops. Key changes: - Switched from chat.completions.create() to client.responses.create() - Use "server_url": "litellm_proxy" to leverage LiteLLM as MCP gateway - Set "require_approval": "never" for fully automatic tool execution - Simplified get_available_mcp_tools() to get_available_mcp_servers() - Removed manual OpenAI tool format conversion (LiteLLM handles this) - Updated response extraction to use output[0].content[0].text format - Convert system prompts to user role for Responses API compatibility Technical improvements: - LiteLLM now handles the complete tool calling loop automatically - No more placeholder responses - actual MCP tools will execute - Cleaner code with ~100 fewer lines - Better separation between tools-enabled and tools-disabled paths - Proper error handling for Responses API format Responses API benefits: - Single API call returns final response with tool results integrated - Automatic tool discovery, execution, and result formatting - No manual tracking of tool_call_ids or conversation state - Native MCP support via server_label configuration Documentation: - Added comprehensive litellm-mcp-research.md with API examples - Documented Responses API vs chat.completions differences - Included Discord bot migration patterns - Covered authentication, streaming, and tool restrictions Next steps: - Test with actual Discord interactions - Verify GitHub MCP tools execute correctly - Monitor response extraction for edge cases 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com>
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@@ -80,8 +80,8 @@ async def download_image(url: str) -> str | None:
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print(f"Error downloading image from {url}: {e}")
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return None
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async def get_available_mcp_tools():
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"""Query LiteLLM for available MCP servers and tools, convert to OpenAI format"""
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async def get_available_mcp_servers():
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"""Query LiteLLM for available MCP servers (used with Responses API)"""
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try:
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base_url = LITELLM_API_BASE.rstrip('/')
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headers = {"x-litellm-api-key": LITELLM_API_KEY}
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@@ -95,50 +95,20 @@ async def get_available_mcp_tools():
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if server_response.status_code == 200:
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server_info = server_response.json()
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debug_log(f"MCP server info: found {len(server_info) if isinstance(server_info, list) else 0} servers")
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server_count = len(server_info) if isinstance(server_info, list) else 0
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debug_log(f"MCP server info: found {server_count} servers")
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# Get available MCP tools
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tools_response = await http_client.get(
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f"{base_url}/v1/mcp/tools",
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headers=headers
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)
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if server_count > 0:
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# Log server names for visibility
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server_names = [s.get("server_name") for s in server_info if isinstance(s, dict) and s.get("server_name")]
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debug_log(f"Available MCP servers: {server_names}")
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if tools_response.status_code == 200:
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tools_data = tools_response.json()
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# Tools come in format: {"tools": [...]}
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mcp_tools = tools_data.get("tools", []) if isinstance(tools_data, dict) else tools_data
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debug_log(f"Found {len(mcp_tools) if isinstance(mcp_tools, list) else 0} MCP tools")
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# Convert MCP tools to OpenAI function calling format
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openai_tools = []
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for tool in mcp_tools[:50]: # Limit to first 50 tools to avoid overwhelming the model
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if isinstance(tool, dict) and "name" in tool:
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openai_tool = {
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"type": "function",
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"function": {
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"name": tool["name"],
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"description": tool.get("description", ""),
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"parameters": tool.get("inputSchema", {})
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}
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}
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openai_tools.append(openai_tool)
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debug_log(f"Converted {len(openai_tools)} tools to OpenAI format")
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# Return both server info and converted tools
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return {
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"server": server_info,
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"tools": openai_tools,
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"tool_count": len(openai_tools)
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}
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else:
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debug_log(f"MCP tools endpoint returned {tools_response.status_code}: {tools_response.text}")
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return {"server": server_info}
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else:
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debug_log(f"MCP server endpoint returned {server_response.status_code}: {server_response.text}")
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except Exception as e:
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debug_log(f"Error fetching MCP tools: {e}")
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debug_log(f"Error fetching MCP servers: {e}")
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return None
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@@ -286,7 +256,7 @@ async def get_chat_history(channel, bot_user_id: int, limit: int = 50) -> List[D
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async def get_ai_response(history_messages: List[Dict[str, Any]], user_message: str, image_urls: List[str] = None) -> str:
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"""
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Get AI response using LiteLLM with proper conversation history and tool calling support.
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Get AI response using LiteLLM Responses API with automatic MCP tool execution.
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Args:
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history_messages: List of previous conversation messages with roles
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@@ -296,89 +266,114 @@ async def get_ai_response(history_messages: List[Dict[str, Any]], user_message:
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Returns:
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AI response string
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"""
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# Start with system prompt
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messages = [{"role": "system", "content": SYSTEM_PROMPT}]
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# Add conversation history
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messages.extend(history_messages)
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# Build current user message
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if image_urls:
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# Multi-modal message with text and images
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content_parts = [{"type": "text", "text": user_message}]
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for url in image_urls:
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base64_image = await download_image(url)
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if base64_image:
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content_parts.append({
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"type": "image_url",
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"image_url": {
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"url": f"data:image/jpeg;base64,{base64_image}"
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}
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})
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messages.append({"role": "user", "content": content_parts})
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else:
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# Text-only message
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messages.append({"role": "user", "content": user_message})
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try:
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# Build request parameters
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request_params = {
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"model": MODEL_NAME,
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"messages": messages,
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"temperature": 0.7,
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}
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# Add MCP tools if enabled
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# When tools are enabled, use Responses API with MCP for automatic tool execution
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if ENABLE_TOOLS:
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debug_log("Tools enabled - fetching and converting MCP tools")
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debug_log("Tools enabled - using Responses API with MCP auto-execution")
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# Query and convert MCP tools to OpenAI format
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mcp_info = await get_available_mcp_tools()
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if mcp_info and isinstance(mcp_info, dict):
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openai_tools = mcp_info.get("tools", [])
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if openai_tools and isinstance(openai_tools, list) and len(openai_tools) > 0:
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request_params["tools"] = openai_tools
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request_params["tool_choice"] = "auto"
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debug_log(f"Added {len(openai_tools)} tools to request")
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else:
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debug_log("No tools available to add to request")
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else:
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debug_log("Failed to fetch MCP tools")
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# Query MCP server info to get server_label
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mcp_info = await get_available_mcp_servers()
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debug_log(f"Calling chat completions with {len(request_params.get('tools', []))} tools")
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response = client.chat.completions.create(**request_params)
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# Build input array with system prompt, history, and current message
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input_messages = []
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# Handle tool calls if present
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response_message = response.choices[0].message
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tool_calls = getattr(response_message, 'tool_calls', None)
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# Add system prompt as developer role (newer models) or user role
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input_messages.append({
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"role": "user", # System messages converted to user for Responses API
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"content": f"[System Instructions]\n{SYSTEM_PROMPT}"
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})
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if tool_calls and len(tool_calls) > 0:
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debug_log(f"Model requested {len(tool_calls)} tool calls")
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# Add assistant's response with tool calls to messages
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messages.append(response_message)
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# Execute each tool call - add placeholder responses
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# TODO: Implement actual MCP tool execution via LiteLLM proxy
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for tool_call in tool_calls:
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function_name = tool_call.function.name
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function_args = tool_call.function.arguments
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debug_log(f"Tool call requested: {function_name} with args: {function_args}")
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# Placeholder response - in production this would execute via MCP
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messages.append({
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"role": "tool",
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"tool_call_id": tool_call.id,
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"name": function_name,
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"content": f"Tool execution via MCP is being set up. Tool {function_name} was called with arguments: {function_args}"
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# Add conversation history
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for msg in history_messages:
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input_messages.append({
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"role": msg["role"],
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"content": msg["content"]
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})
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# Get final response from model after tool execution
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debug_log("Getting final response after tool execution")
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final_response = client.chat.completions.create(**request_params)
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return final_response.choices[0].message.content
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# Build current user message
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if image_urls:
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# Multi-modal message with text and images
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content_parts = [{"type": "text", "text": user_message}]
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for url in image_urls:
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base64_image = await download_image(url)
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if base64_image:
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content_parts.append({
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"type": "image_url",
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"image_url": {
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"url": f"data:image/jpeg;base64,{base64_image}"
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}
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})
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input_messages.append({"role": "user", "content": content_parts})
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else:
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input_messages.append({"role": "user", "content": user_message})
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# Build MCP tools configuration
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tools_config = []
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if mcp_info and isinstance(mcp_info, dict):
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server_list = mcp_info.get("server", [])
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if isinstance(server_list, list) and len(server_list) > 0:
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for server_info in server_list:
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server_name = server_info.get("server_name")
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if server_name:
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tools_config.append({
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"type": "mcp",
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"server_label": server_name,
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"server_url": "litellm_proxy", # Use LiteLLM as MCP gateway
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"require_approval": "never" # Automatic tool execution
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})
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debug_log(f"Added MCP server '{server_name}' with auto-execution")
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if not tools_config:
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debug_log("No MCP servers found, falling back to standard chat completions")
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# Fall through to standard chat completions below
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else:
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# Use Responses API with MCP tools
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debug_log(f"Calling Responses API with {len(tools_config)} MCP servers")
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response = client.responses.create(
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model=MODEL_NAME,
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input=input_messages,
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tools=tools_config,
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stream=False
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)
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debug_log(f"Response status: {response.status}")
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# Extract text from Responses API format
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if hasattr(response, 'output') and len(response.output) > 0:
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for output in response.output:
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if hasattr(output, 'type') and output.type == "message":
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if hasattr(output, 'content') and len(output.content) > 0:
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for content in output.content:
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if hasattr(content, 'type') and content.type == "output_text":
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return content.text
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debug_log(f"Unexpected response format: {response}")
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return "I received a response but couldn't extract the text. Please try again."
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# Standard chat completions (when tools disabled or MCP not available)
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debug_log("Using standard chat completions")
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messages = [{"role": "system", "content": SYSTEM_PROMPT}]
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messages.extend(history_messages)
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if image_urls:
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content_parts = [{"type": "text", "text": user_message}]
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for url in image_urls:
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base64_image = await download_image(url)
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if base64_image:
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content_parts.append({
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"type": "image_url",
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"image_url": {"url": f"data:image/jpeg;base64,{base64_image}"}
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})
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messages.append({"role": "user", "content": content_parts})
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else:
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messages.append({"role": "user", "content": user_message})
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response = client.chat.completions.create(
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model=MODEL_NAME,
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messages=messages,
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temperature=0.7
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)
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return response.choices[0].message.content
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