add

测试mcp基座

agent_loop.py

@@ -0,0 +1,163 @@
+import socket
+import json
+import time
+from openai import OpenAI
+
+# 初始化 DeepSeek 客户端
+client = OpenAI(
+    api_key="sk-420190f448fe41158c4e2ccff90e35ce", 
+    base_url="https://api.deepseek.com" 
+) 
+MODEL_NAME = "deepseek-chat"
+
+def fetch_and_translate_tools():
+    """向 CAD 基座发送 tools/list，并转换为大模型认识的格式"""
+    try:
+        cad_socket = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
+        cad_socket.connect(("127.0.0.1", 8080))
+        
+        # 1. 向 MCP Server 发送标准的 tools/list 请求
+        req = {
+            "jsonrpc": "2.0",
+            "id": 100,
+            "method": "tools/list"
+        }
+        cad_socket.sendall(json.dumps(req).encode('utf-8'))
+        
+        response_data = cad_socket.recv(8192)
+        cad_socket.close()
+        
+        mcp_response = json.loads(response_data.decode('utf-8'))
+        
+        if "result" not in mcp_response or "tools" not in mcp_response["result"]:
+            print("[Error] 无法从 CAD 获取工具列表")
+            return []
+            
+        llm_tools = []
+        # 2. 遍历 CAD 返回的工具，将其翻译为 DeepSeek/OpenAI 格式
+        for mcp_tool in mcp_response["result"]["tools"]:
+            llm_tools.append({
+                "type": "function",
+                "function": {
+                    "name": mcp_tool["name"],
+                    "description": mcp_tool["description"],
+                    # 核心转换：MCP 的 inputSchema 等价于 LLM 的 parameters
+                    "parameters": mcp_tool["inputSchema"] 
+                }
+            })
+            
+        print(f"[Init] 成功从 CAD 动态加载了 {len(llm_tools)} 个工具！")
+        return llm_tools
+        
+    except Exception as e:
+        print(f"[Error] 获取工具列表失败: {e}")
+        return []
+
+def call_cad_mcp_server(tool_name, arguments):
+    """底层 TCP 通信保持不变"""
+    try:
+        cad_socket = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
+        cad_socket.connect(("127.0.0.1", 8080))
+        req = {
+            "jsonrpc": "2.0", "id": 1, "method": "tools/call",
+            "params": { "name": tool_name, "arguments": arguments }
+        }
+        cad_socket.sendall(json.dumps(req).encode('utf-8'))
+        response_data = b""
+        while True:
+            chunk = cad_socket.recv(8192)
+            response_data += chunk
+            if len(chunk) < 8192: break
+        cad_socket.close()
+        return json.loads(response_data.decode('utf-8'))
+    except Exception as e:
+        print(f"[Error] 连接 CAD 基座失败: {e}")
+        return None
+
+def run_agent(user_prompt):
+    print("=== 🚀 CAD 具身智能 Agent 启动 ===")
+
+    # 动态向 CAD 请求可用工具，彻底解耦！
+    tools = fetch_and_translate_tools()
+    
+    if not tools:
+        print("没有可用的工具，Agent 退出。")
+        return
+
+    # 核心心智设定：教它怎么形成视觉闭环
+    system_prompt = """你是一个高阶 AutoCAD 视觉检查专家。
+请严格遵循以下工作流：
+1. 第一步永远是调用 `get_viewport_screenshot` 观察当前画面。
+2. 仔细评估图像。如果目标物体（如图元、文字）太小导致无法确信细节，请调用 `zoom_window_normalized` 放大该局部区域。
+3. 【关键指令】：每次执行缩放操作（zoom）后，你必须在下一回合再次调用 `get_viewport_screenshot` 获取放大后的新截图！
+4. 反复观察和放大，直到你 100% 看清细节，再用自然语言向用户输出最终结论。"""
+
+    messages = [
+        {"role": "system", "content": system_prompt},
+        {"role": "user", "content": user_prompt}
+    ]
+    
+    MAX_TURNS = 8  # 熔断机制：最多允许思考 8 个回合，防止无限套娃
+    
+    for turn in range(MAX_TURNS):
+        print(f"\n--- [第 {turn + 1} 回合] 思考中 ---")
+        
+        response = client.chat.completions.create(
+            model=MODEL_NAME,
+            messages=messages,
+            tools=tools
+        )
+        
+        assistant_message = response.choices[0].message
+        messages.append(assistant_message) # 记录思维路径
+        
+        # 1. 检查是否需要执行物理动作
+        if assistant_message.tool_calls:
+            for tool_call in assistant_message.tool_calls:
+                tool_name = tool_call.function.name
+                args = json.loads(tool_call.function.arguments)
+                
+                print(f"[动作决定] ⚡ 调用工具: {tool_name}")
+                if args: print(f"          参数: {args}")
+                
+                # 执行 CAD 通信
+                cad_res = call_cad_mcp_server(tool_name, args)
+                time.sleep(0.5) # 给予 CAD 渲染刷新窗口的时间缓冲
+                
+                # 处理执行结果，并准备发回给 LLM 的观测报告
+                tool_result_content = []
+                
+                if cad_res and "result" in cad_res:
+                    for item in cad_res["result"]["content"]:
+                        if item["type"] == "text":
+                            tool_result_content.append({"type": "text", "text": item["text"]})
+                        elif item["type"] == "image":
+                            base64_img = item["data"]
+                            mime = item.get("mimeType", "image/png")
+                            tool_result_content.append({
+                                "type": "image_url",
+                                "image_url": {"url": f"data:{mime};base64,{base64_img}"}
+                            })
+                            print("[观测反馈] 📸 已将最新 CAD 屏幕画面传回视觉中枢。")
+                else:
+                    tool_result_content.append({"type": "text", "text": "CAD 工具执行失败或无响应。"})
+                
+                # 必须向大模型提交 Tool 返回结果
+                messages.append({
+                    "role": "tool",
+                    "tool_call_id": tool_call.id,
+                    "content": tool_result_content
+                })
+                
+        # 2. 如果不调工具，说明任务已完成，输出最终自然语言结论
+        else:
+            print("\n==================================")
+            print(f"🎯 [最终结论]:\n{assistant_message.content}")
+            print("==================================")
+            break
+            
+    else:
+        print("\n[警告] 达到最大思考回合数限制，Agent 强行中止。")
+
+if __name__ == "__main__":
+    run_agent("请仔细检查当前图纸，告诉我图纸中心那些小圆的内部，是否还包含了更小的同心圆？如果看不清，请务必放大确认。")

countOfCircle.py

@@ -0,0 +1,127 @@
+import socket
+import json
+from openai import OpenAI
+
+# 1. 初始化 DeepSeek 客户端
+# 注意：务必确保已经执行过 pip install openai
+client = OpenAI(
+    api_key="sk-420190f448fe41158c4e2ccff90e35ce", 
+    base_url="https://api.deepseek.com"  # 核心修改：将网关指向 DeepSeek 服务器
+) 
+
+# 核心修改：使用 DeepSeek 的主模型。
+# (注: DeepSeek-V4/V4.1 的视觉支持已集成，具体模型名请以你 DeepSeek 后台显示的可用模型为准)
+MODEL_NAME = "deepseek-flash" 
+
+def call_cad_mcp_server(tool_name, arguments):
+    """封装与 AutoCAD MCP 基座的 TCP 通信"""
+    try:
+        cad_socket = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
+        cad_socket.connect(("127.0.0.1", 8080))
+        
+        req = {
+            "jsonrpc": "2.0",
+            "id": 1,
+            "method": "tools/call",
+            "params": {
+                "name": tool_name,
+                "arguments": arguments
+            }
+        }
+        cad_socket.sendall(json.dumps(req).encode('utf-8'))
+        
+        response_data = b""
+        while True:
+            chunk = cad_socket.recv(8192)
+            response_data += chunk
+            if len(chunk) < 8192:
+                break
+                
+        cad_socket.close()
+        return json.loads(response_data.decode('utf-8'))
+    except Exception as e:
+        print(f"[Error] 连接 CAD 基座失败: {e}")
+        return None
+
+def main():
+    print("=== DeepSeek CAD Vision Agent 启动 ===")
+    
+    # 2. 定义 MCP 工具 (Tool Calling)
+    tools = [
+        {
+            "type": "function",
+            "function": {
+                "name": "get_viewport_screenshot",
+                "description": "获取当前 AutoCAD 视口的实时截图。当用户询问图纸上的视觉特征、数量或位置时，必须先调用此工具获取画面。"
+            }
+        }
+    ]
+    
+    # 3. 初始化历史
+    messages = [
+        {"role": "system", "content": "你是一个专业的 AutoCAD 视觉审查助手。必须先使用截图工具观察图纸，再回答用户问题。"},
+        {"role": "user", "content": "请看看当前 CAD 屏幕上，我一共画了几个圆？"}
+    ]
+    
+    print("\n[DeepSeek] 正在思考如何完成任务...")
+    
+    # === 第一回合：DeepSeek 思考并决定调用工具 ===
+    response = client.chat.completions.create(
+        model=MODEL_NAME,
+        messages=messages,
+        tools=tools
+    )
+    
+    assistant_message = response.choices[0].message
+    messages.append(assistant_message)
+    
+    # 检查 DeepSeek 是否发起了 Tool Call
+    if assistant_message.tool_calls:
+        tool_call = assistant_message.tool_calls[0]
+        tool_name = tool_call.function.name
+        
+        print(f"[DeepSeek] 决定调用 CAD 工具: {tool_name}")
+        print(f"[CAD] 正在执行截图，请稍候...")
+        
+        # === 与 CAD 基座通信 ===
+        cad_res = call_cad_mcp_server(tool_name, {})
+        
+        if cad_res and "result" in cad_res:
+            content_array = cad_res["result"]["content"]
+            base64_img = ""
+            mime_type = "image/png"
+            for item in content_array:
+                if item["type"] == "image":
+                    base64_img = item["data"]
+                    mime_type = item.get("mimeType", "image/png")
+            
+            print("[CAD] 截图成功！正在将视觉数据传回大模型神经中枢...")
+            
+            # 4. 将 Base64 图片按标准多模态格式塞回历史
+            messages.append({
+                "role": "tool",
+                "tool_call_id": tool_call.id,
+                "content": [
+                    {"type": "text", "text": "这是 AutoCAD 当前界面的实时截图。请根据图片回答用户的问题。"},
+                    {"type": "image_url", "image_url": {"url": f"data:{mime_type};base64,{base64_img}"}}
+                ]
+            })
+            
+            # === 第二回合：DeepSeek “看”图并回答 ===
+            print("[DeepSeek] 正在进行视觉分析...")
+            final_response = client.chat.completions.create(
+                model=MODEL_NAME,
+                messages=messages
+            )
+            
+            print("\n==================================")
+            print(f"[DeepSeek 最终回答]:\n{final_response.choices[0].message.content}")
+            print("==================================")
+            
+        else:
+            print("[Error] CAD 工具执行失败。")
+    else:
+        print(f"[DeepSeek 盲猜]: {assistant_message.content}")
+
+if __name__ == "__main__":
+    main()

src/cad_mcp_plugins/mcp_plugins.cpp

@@ -3,74 +3,157 @@
 #include <Windows.h>
 #include "tchar.h"
 #include "rxregsvc.h"
+#include <gdiplus.h>
+#pragma comment(lib, "gdiplus.lib")
 
 #pragma comment(lib, "rxapi.lib")
 #pragma comment(linker, "/export:acrxGetApiVersion,PRIVATE")
 
-class mcp_Tool_DrawCircleCpp : public mcp_Tool
+namespace
+{
+	// 辅助函数：Base64 编码
+	std::string Base64Encode(const unsigned char *data, size_t length)
+	{
+		static const char encoding_table[] = "ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789+/";
+		size_t out_len = 4 * ((length + 2) / 3);
+		std::string ret(out_len, '\0');
+		size_t i;
+		char *p = const_cast<char *>(ret.c_str());
+
+		for (i = 0; i < length - 2; i += 3)
+		{
+			*p++ = encoding_table[(data[i] >> 2) & 0x3F];
+			*p++ = encoding_table[((data[i] & 0x3) << 4) | ((int)(data[i + 1] & 0xF0) >> 4)];
+			*p++ = encoding_table[((data[i + 1] & 0xF) << 2) | ((int)(data[i + 2] & 0xC0) >> 6)];
+			*p++ = encoding_table[data[i + 2] & 0x3F];
+		}
+		if (i < length)
+		{
+			*p++ = encoding_table[(data[i] >> 2) & 0x3F];
+			if (i == (length - 1))
+			{
+				*p++ = encoding_table[((data[i] & 0x3) << 4)];
+				*p++ = '=';
+			}
+			else
+			{
+				*p++ = encoding_table[((data[i] & 0x3) << 4) | ((int)(data[i + 1] & 0xF0) >> 4)];
+				*p++ = encoding_table[((data[i + 1] & 0xF) << 2)];
+			}
+			*p++ = '=';
+		}
+		return ret;
+	}
+
+	// 辅助函数：获取 GDI+ 图像编码器的 Clsid (用于转 PNG)
+	int GetEncoderClsid(const WCHAR *format, CLSID *pClsid)
+	{
+		UINT num = 0, size = 0;
+		Gdiplus::GetImageEncodersSize(&num, &size);
+		if (size == 0) return -1;
+		Gdiplus::ImageCodecInfo *pImageCodecInfo = (Gdiplus::ImageCodecInfo *)(malloc(size));
+		if (pImageCodecInfo == NULL) return -1;
+		Gdiplus::GetImageEncoders(num, size, pImageCodecInfo);
+		for (UINT j = 0; j < num; ++j)
+		{
+			if (_tcscmp(pImageCodecInfo[j].MimeType, format) == 0)
+			{
+				*pClsid = pImageCodecInfo[j].Clsid;
+				free(pImageCodecInfo);
+				return j;
+			}
+		}
+		free(pImageCodecInfo);
+		return -1;
+	}
+}
+
+class mcp_Tool_ViewportScreenshot: public mcp_Tool
 {
 public:
 	virtual nlohmann::json Execute(const nlohmann::json &args) override
 	{
-		// 1. 从大模型传入的参数中提取坐标和半径
+		// 1. 获取 AutoCAD 当前文档的绘图区句柄
-		double x = args.value("cx", 0.0);
+		HWND hWnd = adsw_acadDocWnd();
-		double y = args.value("cy", 0.0);
+		if (!hWnd)
-		double r = args.value("r", 100.0);
+			return mcp_Tool_Utility::make_error(-32000, L"无法获取CAD绘图区窗口句柄");
 
-		// 2. 获取当前活动文档
+		// 2. 初始化 GDI+
-		AcApDocument* pDoc = acDocManager->curDocument();
+		Gdiplus::GdiplusStartupInput gdiplusStartupInput;
-		if (pDoc == nullptr)
+		ULONG_PTR gdiplusToken;
+		Gdiplus::GdiplusStartup(&gdiplusToken, &gdiplusStartupInput, NULL);
+
+		std::string base64Result;
+		bool success = false;
+
+		// 3. 开始 Windows GDI 抓屏逻辑 (使用花括号限定生命周期)
 		{
-			return { { "error", { { "code", -32000 }, { "message", "当前没有打开的 CAD 文档" } } } };
+			RECT rc;
-		}
+			GetClientRect(hWnd, &rc);
+			int width = rc.right - rc.left;
+			int height = rc.bottom - rc.top;
 
-		// 3. 极其重要：因为调用发起者是隐式窗口消息，并非标准 CAD 命令，必须显式锁文档！
+			HDC hdcScreen = GetDC(hWnd);
-		acDocManager->lockDocument(pDoc);
+			HDC hdcMem = CreateCompatibleDC(hdcScreen);
-		AcDbDatabase* pDb = pDoc->database();
+			HBITMAP hBitmap = CreateCompatibleBitmap(hdcScreen, width, height);
+			HBITMAP hOldBitmap = (HBITMAP)SelectObject(hdcMem, hBitmap);
 
-		AcDbBlockTable* pBlockTable = nullptr;
+			// 将屏幕内容块拷贝到内存位图中
-		Acad::ErrorStatus es = pDb->getBlockTable(pBlockTable, AcDb::kForRead);
+			BitBlt(hdcMem, 0, 0, width, height, hdcScreen, 0, 0, SRCCOPY);
-		
+
-		if (es == Acad::eOk)
+			// 将 GDI 位图转换为 GDI+ 位图
-		{
+			Gdiplus::Bitmap bmp(hBitmap, NULL);
-			AcDbBlockTableRecord* pModelSpace = nullptr;
+
-			es = pBlockTable->getAt(ACDB_MODEL_SPACE, pModelSpace, AcDb::kForWrite);
+			// 创建内存流，将位图以 PNG 格式保存到内存，而非硬盘
-			
+			IStream *pStream = nullptr;
-			if (es == Acad::eOk)
+			if (CreateStreamOnHGlobal(NULL, TRUE, &pStream) == S_OK)
 			{
-				// 在底层 C++ 层面构造实体对象
+				CLSID pngClsid;
-				AcGePoint3d center(x, y, 0.0);
+				GetEncoderClsid(L"image/png", &pngClsid);
-				AcGeVector3d normal(0.0, 0.0, 1.0); // Z轴法线
-				AcDbCircle* pCircle = new AcDbCircle(center, normal, r);
 
-				// 追加到模型空间
+				if (bmp.Save(pStream, &pngClsid, NULL) == Gdiplus::Ok)
-				AcDbObjectId circleId;
+				{
-				pModelSpace->appendAcDbEntity(circleId, pCircle);
+					// 从内存流中读取二进制数据
-				
+					STATSTG stat;
-				// 释放对象
+					pStream->Stat(&stat, STATFLAG_NONAME);
-				pCircle->close();
+					ULONG size = stat.cbSize.LowPart;
-				pModelSpace->close();
+
+					std::vector<BYTE> buffer(size);
+					LARGE_INTEGER liZero = {};
+					pStream->Seek(liZero, STREAM_SEEK_SET, NULL);
+
+					ULONG bytesRead;
+					pStream->Read(buffer.data(), size, &bytesRead);
+
+					// 转换为 Base64
+					base64Result = Base64Encode(buffer.data(), size);
+					success = true;
+				}
+				pStream->Release();
 			}
-			pBlockTable->close();
+
+			// 清理 GDI 资源
+			SelectObject(hdcMem, hOldBitmap);
+			DeleteObject(hBitmap);
+			DeleteDC(hdcMem);
+			ReleaseDC(hWnd, hdcScreen);
 		}
 
-		// 4. 解锁文档并刷新显示
+		// 4. 关闭 GDI+
-		acDocManager->unlockDocument(pDoc);
+		Gdiplus::GdiplusShutdown(gdiplusToken);
-		acedUpdateDisplay(); 
 
-		// 5. 返回标准 MCP 结果，如果失败可以根据 es 的值返回 make_error
+		// 5. 按照 MCP 多模态协议返回数据
-		if (es == Acad::eOk)
+		if (success)
 		{
-			return mcp_Tool_Utility::make_text_result(L"C++底层接口已成功在模型空间生成圆形！");
+			return mcp_Tool_Utility::make_mixed_result(L"已成功截取 AutoCAD 当前视口画面。", base64Result, "image/png");
 		}
 		else
 		{
-			return mcp_Tool_Utility::make_error((int)es, L"ObjectARX 内部错误");
+			return mcp_Tool_Utility::make_error(-32001, L"截图或图像编码失败");
 		}
 	}
 };
 
-// 使用宏导出 C 风格工厂函数，供基座的 LoadLibrary 解析
+EXPORT_MCP_TOOL(mcp_Tool_ViewportScreenshot, CreateViewportScreenshotTool)
-EXPORT_MCP_TOOL(mcp_Tool_DrawCircleCpp, CreateDrawCircleToolCpp)
 
 

test.py

@@ -0,0 +1,41 @@
+import socket
+import json
+
+def test_fake_llm_zoom():
+    print("=== 模拟 LLM 发起缩放指令测试 ===")
+    
+    try:
+        client = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
+        client.connect(("127.0.0.1", 8080))
+        
+        # 伪造大模型生成的 tool_call 报文
+        req = {
+            "jsonrpc": "2.0",
+            "id": 999,
+            "method": "tools/call",
+            "params": {
+                "name": "zoom_window_normalized",
+                "arguments": {
+                    "x1": 0.4,
+                    "y1": 0.4,
+                    "x2": 0.6,
+                    "y2": 0.6
+                }
+            }
+        }
+        
+        print(f"\n[发送] 请求参数:\n{json.dumps(req['params'], indent=2)}")
+        client.sendall(json.dumps(req).encode('utf-8'))
+        
+        response_data = client.recv(4096)
+        
+        print(f"\n[接收] CAD 基座返回:")
+        print(json.dumps(json.loads(response_data.decode('utf-8')), indent=2, ensure_ascii=False))
+        
+        client.close()
+        
+    except Exception as e:
+        print(f"测试失败: {e}")
+
+if __name__ == "__main__":
+    test_fake_llm_zoom()

test_autoagent.py

@@ -1,64 +1,43 @@
 import socket
 import json
-import time
+import base64
 
-def send_request(req_data, silent=False):
+def test_screenshot():
-    try:
+    client = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
-        client = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
+    client.connect(("127.0.0.1", 8080))
-        client.connect(("127.0.0.1", 8080))
+    
-        
+    req = {
-        # 发送 UTF-8 编码的 JSON 请求
-        client.sendall(json.dumps(req_data).encode('utf-8'))
-        
-        # 接收并解码结果
-        response = client.recv(4096)
-        
-        if not silent:
-            print(">>> 收到回复:")
-            print(json.dumps(json.loads(response.decode('utf-8')), indent=2, ensure_ascii=False))
-            print("-" * 50)
-            
-        client.close()
-    except Exception as e:
-        print(f"连接失败: {e}")
-
-if __name__ == "__main__":
-    print("=== 测试 1: 验证 C++ DLL 工具调用 ===")
-    request_call = {
         "jsonrpc": "2.0",
-        "id": 4,
+        "id": 5,
         "method": "tools/call",
         "params": {
-            "name": "draw_circle_cpp",
+            "name": "get_viewport_screenshot",
-            "arguments": {
+            "arguments": {}
-                "cx": 2000.0,
-                "cy": 2000.0,
-                "r": 500.0
-            }
         }
     }
-    send_request(request_call)
+    client.sendall(json.dumps(req).encode('utf-8'))
     
-    # 稍作停顿，方便观察 CAD 屏幕
+    # 图片 base64 报文可能很大，需要循环接收直到拿完
-    time.sleep(1)
+    response_data = b""
+    while True:
+        chunk = client.recv(8192)
+        response_data += chunk
+        if len(chunk) < 8192:
+            break
+            
+    client.close()
     
-    print("\n=== 测试 2: C++ 并发写入压力测试 (生成10个同心圆) ===")
+    resp_json = json.loads(response_data.decode('utf-8'))
-    for i in range(10):
-        radius = 100.0 + i * 50.0
-        req = {
-            "jsonrpc": "2.0",
-            "id": 100 + i,
-            "method": "tools/call",
-            "params": {
-                "name": "draw_circle_cpp",
-                "arguments": {
-                    "cx": 4000.0,
-                    "cy": 2000.0,
-                    "r": radius
-                }
-            }
-        }
-        # 连续快速发送，不打印详细返回以模拟极限并发
-        send_request(req, silent=True)
     
-    print(">>> 并发指令发送完毕，请检查 CAD 屏幕。")
+    # 解析并保存图片
+    content_list = resp_json["result"]["content"]
+    for item in content_list:
+        if item["type"] == "image":
+            base64_data = item["data"]
+            image_bytes = base64.b64decode(base64_data)
+            with open("test_screenshot.png", "wb") as f:
+                f.write(image_bytes)
+            print("截图已保存为 test_screenshot.png，请在当前目录查看！")
+
+if __name__ == "__main__":
+    test_screenshot()