MCP Development Framework

MIT License
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{
    "sourceFile": "mcp_tool/tools/llm_tool.py",
    "activeCommit": 0,
    "commits": [
        {
            "activePatchIndex": 7,
            "patches": [
                {
                    "date": 1741527987944,
                    "content": "Index: \n===================================================================\n--- \n+++ \n"
                },
                {
                    "date": 1741528008639,
                    "content": "Index: \n===================================================================\n--- \n+++ \n@@ -47,45 +47,41 @@\n     DEFAULT_MODEL = \"glm-4\"\n     \n     async def execute(self, arguments: Dict[str, Any]) -> List[types.TextContent | types.ImageContent | types.EmbeddedResource]:\n         \"\"\"\n-        分析文档内容\n+        分析文档内容或回答问题\n         \n         Args:\n-            arguments: 参数字典，必须包含'file_path'和'question'键\n+            arguments: 参数字典，必须包含'question'键，可选包含'file_path'和'model'键\n             \n         Returns:\n-            分析结果\n+            分析结果或回答\n         \"\"\"\n         # 参数验证\n-        if \"file_path\" not in arguments:\n-            return [types.TextContent(\n-                type=\"text\",\n-                text=\"错误: 缺少必要参数 'file_path'\"\n-            )]\n-        \n         if \"question\" not in arguments:\n             return [types.TextContent(\n                 type=\"text\",\n                 text=\"错误: 缺少必要参数 'question'\"\n             )]\n         \n-        file_path = arguments[\"file_path\"]\n         question = arguments[\"question\"]\n         model = arguments.get(\"model\", self.DEFAULT_MODEL)\n         \n-        # 检查文件是否存在\n-        if not os.path.exists(file_path):\n-            return [types.TextContent(\n-                type=\"text\",\n-                text=f\"错误: 文件不存在: {file_path}\\n请检查路径是否正确，并确保文件可访问。\"\n-            )]\n-        \n-        try:\n+        # 检查是否提供了文件路径\n+        if \"file_path\" in arguments and arguments[\"file_path\"]:\n+            file_path = arguments[\"file_path\"]\n+            # 检查文件是否存在（如果是本地文件）\n+            is_url = file_path.startswith(('http://', 'https://'))\n+            if not is_url and not os.path.exists(file_path):\n+                return [types.TextContent(\n+                    type=\"text\",\n+                    text=f\"错误: 文件不存在: {file_path}\\n请检查路径是否正确，并确保文件可访问。\"\n+                )]\n+            \n             # 添加处理信息\n             results = [types.TextContent(\n                 type=\"text\",\n-                text=f\"# 文档分析\\n\\n正在分析文件: {os.path.basename(file_path)}\\n问题: {question}\\n\\n请稍候...\"\n+                text=f\"# 文档分析\\n\\n正在分析{'远程文件' if is_url else '文件'}: {os.path.basename(file_path) if not is_url else file_path}\\n问题: {question}\\n\\n请稍候...\"\n             )]\n             \n             # 调用外部API分析文档\n             analysis_result = await self._analyze_document(file_path, question, model)\n@@ -96,15 +92,25 @@\n                 text=f\"## 分析结果\\n\\n{analysis_result}\"\n             ))\n             \n             return results\n-        except Exception as e:\n-            import traceback\n-            error_details = traceback.format_exc()\n-            return [types.TextContent(\n+        else:\n+            # 直接提问模式，不涉及文档\n+            results = [types.TextContent(\n                 type=\"text\",\n-                text=f\"错误: 分析文档失败: {str(e)}\\n详细错误信息: {error_details}\"\n+                text=f\"# 问答\\n\\n问题: {question}\\n\\n请稍候...\"\n             )]\n+            \n+            # 调用外部API回答问题\n+            answer = await self._ask_question(question, model)\n+            \n+            # 添加回答结果\n+            results.append(types.TextContent(\n+                type=\"text\",\n+                text=f\"## 回答\\n\\n{answer}\"\n+            ))\n+            \n+            return results\n     \n     async def _analyze_document(self, file_path: str, question: str, model: str) -> str:\n         \"\"\"\n         调用外部API分析文档\n"
                },
                {
                    "date": 1741528034034,
                    "content": "Index: \n===================================================================\n--- \n+++ \n@@ -201,5 +201,72 @@\n             return f\"API请求错误: {str(e)}\"\n         except Exception as e:\n             import traceback\n             error_details = traceback.format_exc()\n+            return f\"调用API时出错: {str(e)}\\n详细错误信息: {error_details}\" \n+\n+    async def _ask_question(self, question: str, model: str) -> str:\n+        \"\"\"\n+        调用外部API直接回答问题\n+        \n+        Args:\n+            question: 问题\n+            model: 模型名称\n+            \n+        Returns:\n+            回答结果\n+        \"\"\"\n+        try:\n+            # 使用requests库直接发送请求\n+            url = f\"{self.BASE_URL}/v1/chat/completions\"\n+            \n+            # 构建请求数据\n+            payload = {\n+                \"model\": model,\n+                \"messages\": [\n+                    {\n+                        \"role\": \"user\",\n+                        \"content\": question\n+                    }\n+                ],\n+                \"stream\": False\n+            }\n+            \n+            # 设置请求头\n+            headers = {\n+                'Accept': 'application/json',\n+                'Authorization': f'Bearer {self.API_KEY}',\n+                'Content-Type': 'application/json'\n+            }\n+            \n+            # 发送请求\n+            response = requests.post(url, json=payload, headers=headers, timeout=60)\n+            \n+            # 检查响应状态码\n+            if response.status_code != 200:\n+                try:\n+                    error_data = response.json()\n+                    error_message = error_data.get(\"message\", \"未知错误\")\n+                    return f\"API请求失败 (状态码: {response.status_code}): {error_message}\"\n+                except:\n+                    return f\"API请求失败 (状态码: {response.status_code}): {response.text[:200]}...\"\n+            \n+            # 解析响应\n+            try:\n+                response_data = response.json()\n+            except json.JSONDecodeError:\n+                return f\"无法解析API响应: {response.text[:200]}...\"\n+            \n+            # 提取回答结果\n+            if \"choices\" in response_data and len(response_data[\"choices\"]) > 0:\n+                message = response_data[\"choices\"][0].get(\"message\", {})\n+                content = message.get(\"content\", \"未能获取回答\")\n+                return content\n+            else:\n+                return f\"未能获取回答，API响应格式不符合预期: {json.dumps(response_data)[:200]}...\"\n+            \n+        except requests.exceptions.RequestException as e:\n+            return f\"API请求错误: {str(e)}\"\n+        except Exception as e:\n+            import traceback\n+            error_details = traceback.format_exc()\n             return f\"调用API时出错: {str(e)}\\n详细错误信息: {error_details}\" \n\\ No newline at end of file\n"
                },
                {
                    "date": 1741528083240,
                    "content": "Index: \n===================================================================\n--- \n+++ \n@@ -1,6 +1,6 @@\n \"\"\"\n-大语言模型交互工具，使用外部大模型API分析文档内容或回答问题\n+文档分析工具，使用外部大模型API分析文档内容\n \"\"\"\n \n import os\n import json\n@@ -14,26 +14,26 @@\n import mcp.types as types\n from . import BaseTool, ToolRegistry\n \n @ToolRegistry.register\n-class LLMTool(BaseTool):\n+class DocAnalysisTool(BaseTool):\n     \"\"\"\n-    大语言模型交互工具，使用外部大模型API分析文档内容或回答问题\n+    文档分析工具，使用外部大模型API分析文档内容\n     \"\"\"\n     \n     name = \"llm\"\n     description = \"使用大模型分析文档内容或回答问题，支持PDF、Word等格式\"\n     input_schema = {\n         \"type\": \"object\",\n-        \"required\": [\"question\"],\n+        \"required\": [\"file_path\", \"question\"],\n         \"properties\": {\n             \"file_path\": {\n                 \"type\": \"string\",\n                 \"description\": \"文档的本地路径或URL，例如'/path/to/document.pdf'或'https://example.com/document.pdf'\",\n             },\n             \"question\": {\n                 \"type\": \"string\",\n-                \"description\": \"关于文档的问题或直接提问，例如'文档里说了什么？'或'什么是人工智能？'\",\n+                \"description\": \"关于文档的问题，例如'文档里说了什么？'\",\n             },\n             \"model\": {\n                 \"type\": \"string\",\n                 \"description\": \"使用的模型名称，默认为'glm-4'\",\n@@ -47,41 +47,45 @@\n     DEFAULT_MODEL = \"glm-4\"\n     \n     async def execute(self, arguments: Dict[str, Any]) -> List[types.TextContent | types.ImageContent | types.EmbeddedResource]:\n         \"\"\"\n-        分析文档内容或回答问题\n+        分析文档内容\n         \n         Args:\n-            arguments: 参数字典，必须包含'question'键，可选包含'file_path'和'model'键\n+            arguments: 参数字典，必须包含'file_path'和'question'键\n             \n         Returns:\n-            分析结果或回答\n+            分析结果\n         \"\"\"\n         # 参数验证\n+        if \"file_path\" not in arguments:\n+            return [types.TextContent(\n+                type=\"text\",\n+                text=\"错误: 缺少必要参数 'file_path'\"\n+            )]\n+        \n         if \"question\" not in arguments:\n             return [types.TextContent(\n                 type=\"text\",\n                 text=\"错误: 缺少必要参数 'question'\"\n             )]\n         \n+        file_path = arguments[\"file_path\"]\n         question = arguments[\"question\"]\n         model = arguments.get(\"model\", self.DEFAULT_MODEL)\n         \n-        # 检查是否提供了文件路径\n-        if \"file_path\" in arguments and arguments[\"file_path\"]:\n-            file_path = arguments[\"file_path\"]\n-            # 检查文件是否存在（如果是本地文件）\n-            is_url = file_path.startswith(('http://', 'https://'))\n-            if not is_url and not os.path.exists(file_path):\n-                return [types.TextContent(\n-                    type=\"text\",\n-                    text=f\"错误: 文件不存在: {file_path}\\n请检查路径是否正确，并确保文件可访问。\"\n-                )]\n-            \n+        # 检查文件是否存在\n+        if not os.path.exists(file_path):\n+            return [types.TextContent(\n+                type=\"text\",\n+                text=f\"错误: 文件不存在: {file_path}\\n请检查路径是否正确，并确保文件可访问。\"\n+            )]\n+        \n+        try:\n             # 添加处理信息\n             results = [types.TextContent(\n                 type=\"text\",\n-                text=f\"# 文档分析\\n\\n正在分析{'远程文件' if is_url else '文件'}: {os.path.basename(file_path) if not is_url else file_path}\\n问题: {question}\\n\\n请稍候...\"\n+                text=f\"# 文档分析\\n\\n正在分析文件: {os.path.basename(file_path)}\\n问题: {question}\\n\\n请稍候...\"\n             )]\n             \n             # 调用外部API分析文档\n             analysis_result = await self._analyze_document(file_path, question, model)\n@@ -92,25 +96,15 @@\n                 text=f\"## 分析结果\\n\\n{analysis_result}\"\n             ))\n             \n             return results\n-        else:\n-            # 直接提问模式，不涉及文档\n-            results = [types.TextContent(\n+        except Exception as e:\n+            import traceback\n+            error_details = traceback.format_exc()\n+            return [types.TextContent(\n                 type=\"text\",\n-                text=f\"# 问答\\n\\n问题: {question}\\n\\n请稍候...\"\n+                text=f\"错误: 分析文档失败: {str(e)}\\n详细错误信息: {error_details}\"\n             )]\n-            \n-            # 调用外部API回答问题\n-            answer = await self._ask_question(question, model)\n-            \n-            # 添加回答结果\n-            results.append(types.TextContent(\n-                type=\"text\",\n-                text=f\"## 回答\\n\\n{answer}\"\n-            ))\n-            \n-            return results\n     \n     async def _analyze_document(self, file_path: str, question: str, model: str) -> str:\n         \"\"\"\n         调用外部API分析文档\n@@ -201,72 +195,5 @@\n             return f\"API请求错误: {str(e)}\"\n         except Exception as e:\n             import traceback\n             error_details = traceback.format_exc()\n-            return f\"调用API时出错: {str(e)}\\n详细错误信息: {error_details}\" \n-\n-    async def _ask_question(self, question: str, model: str) -> str:\n-        \"\"\"\n-        调用外部API直接回答问题\n-        \n-        Args:\n-            question: 问题\n-            model: 模型名称\n-            \n-        Returns:\n-            回答结果\n-        \"\"\"\n-        try:\n-            # 使用requests库直接发送请求\n-            url = f\"{self.BASE_URL}/v1/chat/completions\"\n-            \n-            # 构建请求数据\n-            payload = {\n-                \"model\": model,\n-                \"messages\": [\n-                    {\n-                        \"role\": \"user\",\n-                        \"content\": question\n-                    }\n-                ],\n-                \"stream\": False\n-            }\n-            \n-            # 设置请求头\n-            headers = {\n-                'Accept': 'application/json',\n-                'Authorization': f'Bearer {self.API_KEY}',\n-                'Content-Type': 'application/json'\n-            }\n-            \n-            # 发送请求\n-            response = requests.post(url, json=payload, headers=headers, timeout=60)\n-            \n-            # 检查响应状态码\n-            if response.status_code != 200:\n-                try:\n-                    error_data = response.json()\n-                    error_message = error_data.get(\"message\", \"未知错误\")\n-                    return f\"API请求失败 (状态码: {response.status_code}): {error_message}\"\n-                except:\n-                    return f\"API请求失败 (状态码: {response.status_code}): {response.text[:200]}...\"\n-            \n-            # 解析响应\n-            try:\n-                response_data = response.json()\n-            except json.JSONDecodeError:\n-                return f\"无法解析API响应: {response.text[:200]}...\"\n-            \n-            # 提取回答结果\n-            if \"choices\" in response_data and len(response_data[\"choices\"]) > 0:\n-                message = response_data[\"choices\"][0].get(\"message\", {})\n-                content = message.get(\"content\", \"未能获取回答\")\n-                return content\n-            else:\n-                return f\"未能获取回答，API响应格式不符合预期: {json.dumps(response_data)[:200]}...\"\n-            \n-        except requests.exceptions.RequestException as e:\n-            return f\"API请求错误: {str(e)}\"\n-        except Exception as e:\n-            import traceback\n-            error_details = traceback.format_exc()\n             return f\"调用API时出错: {str(e)}\\n详细错误信息: {error_details}\" \n\\ No newline at end of file\n"
                },
                {
                    "date": 1741528567167,
                    "content": "Index: \n===================================================================\n--- \n+++ \n@@ -47,64 +47,89 @@\n     DEFAULT_MODEL = \"glm-4\"\n     \n     async def execute(self, arguments: Dict[str, Any]) -> List[types.TextContent | types.ImageContent | types.EmbeddedResource]:\n         \"\"\"\n-        分析文档内容\n+        分析文档内容或回答问题\n         \n         Args:\n-            arguments: 参数字典，必须包含'file_path'和'question'键\n+            arguments: 参数字典，可以包含'file_path'和'question'键\n             \n         Returns:\n             分析结果\n         \"\"\"\n         # 参数验证\n-        if \"file_path\" not in arguments:\n-            return [types.TextContent(\n-                type=\"text\",\n-                text=\"错误: 缺少必要参数 'file_path'\"\n-            )]\n-        \n         if \"question\" not in arguments:\n             return [types.TextContent(\n                 type=\"text\",\n                 text=\"错误: 缺少必要参数 'question'\"\n             )]\n         \n-        file_path = arguments[\"file_path\"]\n         question = arguments[\"question\"]\n         model = arguments.get(\"model\", self.DEFAULT_MODEL)\n         \n-        # 检查文件是否存在\n-        if not os.path.exists(file_path):\n-            return [types.TextContent(\n-                type=\"text\",\n-                text=f\"错误: 文件不存在: {file_path}\\n请检查路径是否正确，并确保文件可访问。\"\n-            )]\n-        \n-        try:\n-            # 添加处理信息\n-            results = [types.TextContent(\n-                type=\"text\",\n-                text=f\"# 文档分析\\n\\n正在分析文件: {os.path.basename(file_path)}\\n问题: {question}\\n\\n请稍候...\"\n-            )]\n+        # 检查是否提供了文件路径\n+        if \"file_path\" in arguments:\n+            file_path = arguments[\"file_path\"]\n+            # 检查文件路径是否是URL\n+            is_url = file_path.startswith(('http://', 'https://'))\n             \n-            # 调用外部API分析文档\n-            analysis_result = await self._analyze_document(file_path, question, model)\n+            # 如果不是URL，检查文件是否存在\n+            if not is_url and not os.path.exists(file_path):\n+                return [types.TextContent(\n+                    type=\"text\",\n+                    text=f\"错误: 文件不存在: {file_path}\\n请检查路径是否正确，并确保文件可访问。\\n如果使用Docker，请确保文件路径在容器内可访问，例如使用挂载的目录。\"\n+                )]\n             \n-            # 添加分析结果\n-            results.append(types.TextContent(\n-                type=\"text\",\n-                text=f\"## 分析结果\\n\\n{analysis_result}\"\n-            ))\n-            \n-            return results\n-        except Exception as e:\n-            import traceback\n-            error_details = traceback.format_exc()\n-            return [types.TextContent(\n-                type=\"text\",\n-                text=f\"错误: 分析文档失败: {str(e)}\\n详细错误信息: {error_details}\"\n-            )]\n+            try:\n+                # 添加处理信息\n+                results = [types.TextContent(\n+                    type=\"text\",\n+                    text=f\"# 文档分析\\n\\n正在分析文件: {os.path.basename(file_path) if not is_url else file_path}\\n问题: {question}\\n\\n请稍候...\"\n+                )]\n+                \n+                # 调用外部API分析文档\n+                analysis_result = await self._analyze_document(file_path, question, model)\n+                \n+                # 添加分析结果\n+                results.append(types.TextContent(\n+                    type=\"text\",\n+                    text=f\"## 分析结果\\n\\n{analysis_result}\"\n+                ))\n+                \n+                return results\n+            except Exception as e:\n+                import traceback\n+                error_details = traceback.format_exc()\n+                return [types.TextContent(\n+                    type=\"text\",\n+                    text=f\"错误: 分析文档失败: {str(e)}\\n详细错误信息: {error_details}\"\n+                )]\n+        else:\n+            # 直接回答问题，不分析文档\n+            try:\n+                # 添加处理信息\n+                results = [types.TextContent(\n+                    type=\"text\",\n+                    text=f\"# 问题\\n\\n{question}\\n\\n请稍候...\"\n+                )]\n+                \n+                # 调用外部API回答问题\n+                answer = await self._ask_question(question, model)\n+                \n+                # 添加回答结果\n+                results.append(types.TextContent(\n+                    type=\"text\",\n+                    text=f\"## 回答\\n\\n{answer}\"\n+                ))\n+                \n+                return results\n+            except Exception as e:\n+                import traceback\n+                error_details = traceback.format_exc()\n+                return [types.TextContent(\n+                    type=\"text\",\n+                    text=f\"错误: 回答问题失败: {str(e)}\\n详细错误信息: {error_details}\"\n+                )]\n     \n     async def _analyze_document(self, file_path: str, question: str, model: str) -> str:\n         \"\"\"\n         调用外部API分析文档\n@@ -195,5 +220,72 @@\n             return f\"API请求错误: {str(e)}\"\n         except Exception as e:\n             import traceback\n             error_details = traceback.format_exc()\n+            return f\"调用API时出错: {str(e)}\\n详细错误信息: {error_details}\"\n+    \n+    async def _ask_question(self, question: str, model: str) -> str:\n+        \"\"\"\n+        调用外部API回答问题\n+        \n+        Args:\n+            question: 问题\n+            model: 模型名称\n+            \n+        Returns:\n+            回答结果\n+        \"\"\"\n+        try:\n+            # 使用requests库直接发送请求\n+            url = f\"{self.BASE_URL}/v1/chat/completions\"\n+            \n+            # 构建请求数据\n+            payload = {\n+                \"model\": model,\n+                \"messages\": [\n+                    {\n+                        \"role\": \"user\",\n+                        \"content\": question\n+                    }\n+                ],\n+                \"stream\": False\n+            }\n+            \n+            # 设置请求头\n+            headers = {\n+                'Accept': 'application/json',\n+                'Authorization': f'Bearer {self.API_KEY}',\n+                'Content-Type': 'application/json'\n+            }\n+            \n+            # 发送请求\n+            response = requests.post(url, json=payload, headers=headers, timeout=60)\n+            \n+            # 检查响应状态码\n+            if response.status_code != 200:\n+                try:\n+                    error_data = response.json()\n+                    error_message = error_data.get(\"message\", \"未知错误\")\n+                    return f\"API请求失败 (状态码: {response.status_code}): {error_message}\"\n+                except:\n+                    return f\"API请求失败 (状态码: {response.status_code}): {response.text[:200]}...\"\n+            \n+            # 解析响应\n+            try:\n+                response_data = response.json()\n+            except json.JSONDecodeError:\n+                return f\"无法解析API响应: {response.text[:200]}...\"\n+            \n+            # 提取回答结果\n+            if \"choices\" in response_data and len(response_data[\"choices\"]) > 0:\n+                message = response_data[\"choices\"][0].get(\"message\", {})\n+                content = message.get(\"content\", \"未能获取回答结果\")\n+                return content\n+            else:\n+                return f\"未能获取回答结果，API响应格式不符合预期: {json.dumps(response_data)[:200]}...\"\n+            \n+        except requests.exceptions.RequestException as e:\n+            return f\"API请求错误: {str(e)}\"\n+        except Exception as e:\n+            import traceback\n+            error_details = traceback.format_exc()\n             return f\"调用API时出错: {str(e)}\\n详细错误信息: {error_details}\" \n\\ No newline at end of file\n"
                },
                {
                    "date": 1741528589156,
                    "content": "Index: \n===================================================================\n--- \n+++ \n@@ -23,17 +23,17 @@\n     name = \"llm\"\n     description = \"使用大模型分析文档内容或回答问题，支持PDF、Word等格式\"\n     input_schema = {\n         \"type\": \"object\",\n-        \"required\": [\"file_path\", \"question\"],\n+        \"required\": [\"question\"],\n         \"properties\": {\n             \"file_path\": {\n                 \"type\": \"string\",\n-                \"description\": \"文档的本地路径或URL，例如'/path/to/document.pdf'或'https://example.com/document.pdf'\",\n+                \"description\": \"文档的本地路径或URL，例如'/path/to/document.pdf'或'https://example.com/document.pdf'（可选，如果只是提问则不需要）\",\n             },\n             \"question\": {\n                 \"type\": \"string\",\n-                \"description\": \"关于文档的问题，例如'文档里说了什么？'\",\n+                \"description\": \"关于文档的问题或直接提问，例如'文档里说了什么？'或'什么是人工智能？'\",\n             },\n             \"model\": {\n                 \"type\": \"string\",\n                 \"description\": \"使用的模型名称，默认为'glm-4'\",\n"
                },
                {
                    "date": 1741529059568,
                    "content": "Index: \n===================================================================\n--- \n+++ \n@@ -23,17 +23,17 @@\n     name = \"llm\"\n     description = \"使用大模型分析文档内容或回答问题，支持PDF、Word等格式\"\n     input_schema = {\n         \"type\": \"object\",\n-        \"required\": [\"question\"],\n+        \"required\": [\"file_path\", \"question\"],\n         \"properties\": {\n             \"file_path\": {\n                 \"type\": \"string\",\n-                \"description\": \"文档的本地路径或URL，例如'/path/to/document.pdf'或'https://example.com/document.pdf'（可选，如果只是提问则不需要）\",\n+                \"description\": \"文档的本地路径或URL，例如'/path/to/document.pdf'或'https://example.com/document.pdf'\",\n             },\n             \"question\": {\n                 \"type\": \"string\",\n-                \"description\": \"关于文档的问题或直接提问，例如'文档里说了什么？'或'什么是人工智能？'\",\n+                \"description\": \"关于文档的问题，例如'文档里说了什么？'\",\n             },\n             \"model\": {\n                 \"type\": \"string\",\n                 \"description\": \"使用的模型名称，默认为'glm-4'\",\n@@ -47,89 +47,64 @@\n     DEFAULT_MODEL = \"glm-4\"\n     \n     async def execute(self, arguments: Dict[str, Any]) -> List[types.TextContent | types.ImageContent | types.EmbeddedResource]:\n         \"\"\"\n-        分析文档内容或回答问题\n+        分析文档内容\n         \n         Args:\n-            arguments: 参数字典，可以包含'file_path'和'question'键\n+            arguments: 参数字典，必须包含'file_path'和'question'键\n             \n         Returns:\n             分析结果\n         \"\"\"\n         # 参数验证\n+        if \"file_path\" not in arguments:\n+            return [types.TextContent(\n+                type=\"text\",\n+                text=\"错误: 缺少必要参数 'file_path'\"\n+            )]\n+        \n         if \"question\" not in arguments:\n             return [types.TextContent(\n                 type=\"text\",\n                 text=\"错误: 缺少必要参数 'question'\"\n             )]\n         \n+        file_path = arguments[\"file_path\"]\n         question = arguments[\"question\"]\n         model = arguments.get(\"model\", self.DEFAULT_MODEL)\n         \n-        # 检查是否提供了文件路径\n-        if \"file_path\" in arguments:\n-            file_path = arguments[\"file_path\"]\n-            # 检查文件路径是否是URL\n-            is_url = file_path.startswith(('http://', 'https://'))\n+        # 检查文件是否存在\n+        if not os.path.exists(file_path):\n+            return [types.TextContent(\n+                type=\"text\",\n+                text=f\"错误: 文件不存在: {file_path}\\n请检查路径是否正确，并确保文件可访问。\"\n+            )]\n+        \n+        try:\n+            # 添加处理信息\n+            results = [types.TextContent(\n+                type=\"text\",\n+                text=f\"# 文档分析\\n\\n正在分析文件: {os.path.basename(file_path)}\\n问题: {question}\\n\\n请稍候...\"\n+            )]\n             \n-            # 如果不是URL，检查文件是否存在\n-            if not is_url and not os.path.exists(file_path):\n-                return [types.TextContent(\n-                    type=\"text\",\n-                    text=f\"错误: 文件不存在: {file_path}\\n请检查路径是否正确，并确保文件可访问。\\n如果使用Docker，请确保文件路径在容器内可访问，例如使用挂载的目录。\"\n-                )]\n+            # 调用外部API分析文档\n+            analysis_result = await self._analyze_document(file_path, question, model)\n             \n-            try:\n-                # 添加处理信息\n-                results = [types.TextContent(\n-                    type=\"text\",\n-                    text=f\"# 文档分析\\n\\n正在分析文件: {os.path.basename(file_path) if not is_url else file_path}\\n问题: {question}\\n\\n请稍候...\"\n-                )]\n-                \n-                # 调用外部API分析文档\n-                analysis_result = await self._analyze_document(file_path, question, model)\n-                \n-                # 添加分析结果\n-                results.append(types.TextContent(\n-                    type=\"text\",\n-                    text=f\"## 分析结果\\n\\n{analysis_result}\"\n-                ))\n-                \n-                return results\n-            except Exception as e:\n-                import traceback\n-                error_details = traceback.format_exc()\n-                return [types.TextContent(\n-                    type=\"text\",\n-                    text=f\"错误: 分析文档失败: {str(e)}\\n详细错误信息: {error_details}\"\n-                )]\n-        else:\n-            # 直接回答问题，不分析文档\n-            try:\n-                # 添加处理信息\n-                results = [types.TextContent(\n-                    type=\"text\",\n-                    text=f\"# 问题\\n\\n{question}\\n\\n请稍候...\"\n-                )]\n-                \n-                # 调用外部API回答问题\n-                answer = await self._ask_question(question, model)\n-                \n-                # 添加回答结果\n-                results.append(types.TextContent(\n-                    type=\"text\",\n-                    text=f\"## 回答\\n\\n{answer}\"\n-                ))\n-                \n-                return results\n-            except Exception as e:\n-                import traceback\n-                error_details = traceback.format_exc()\n-                return [types.TextContent(\n-                    type=\"text\",\n-                    text=f\"错误: 回答问题失败: {str(e)}\\n详细错误信息: {error_details}\"\n-                )]\n+            # 添加分析结果\n+            results.append(types.TextContent(\n+                type=\"text\",\n+                text=f\"## 分析结果\\n\\n{analysis_result}\"\n+            ))\n+            \n+            return results\n+        except Exception as e:\n+            import traceback\n+            error_details = traceback.format_exc()\n+            return [types.TextContent(\n+                type=\"text\",\n+                text=f\"错误: 分析文档失败: {str(e)}\\n详细错误信息: {error_details}\"\n+            )]\n     \n     async def _analyze_document(self, file_path: str, question: str, model: str) -> str:\n         \"\"\"\n         调用外部API分析文档\n@@ -220,72 +195,5 @@\n             return f\"API请求错误: {str(e)}\"\n         except Exception as e:\n             import traceback\n             error_details = traceback.format_exc()\n-            return f\"调用API时出错: {str(e)}\\n详细错误信息: {error_details}\"\n-    \n-    async def _ask_question(self, question: str, model: str) -> str:\n-        \"\"\"\n-        调用外部API回答问题\n-        \n-        Args:\n-            question: 问题\n-            model: 模型名称\n-            \n-        Returns:\n-            回答结果\n-        \"\"\"\n-        try:\n-            # 使用requests库直接发送请求\n-            url = f\"{self.BASE_URL}/v1/chat/completions\"\n-            \n-            # 构建请求数据\n-            payload = {\n-                \"model\": model,\n-                \"messages\": [\n-                    {\n-                        \"role\": \"user\",\n-                        \"content\": question\n-                    }\n-                ],\n-                \"stream\": False\n-            }\n-            \n-            # 设置请求头\n-            headers = {\n-                'Accept': 'application/json',\n-                'Authorization': f'Bearer {self.API_KEY}',\n-                'Content-Type': 'application/json'\n-            }\n-            \n-            # 发送请求\n-            response = requests.post(url, json=payload, headers=headers, timeout=60)\n-            \n-            # 检查响应状态码\n-            if response.status_code != 200:\n-                try:\n-                    error_data = response.json()\n-                    error_message = error_data.get(\"message\", \"未知错误\")\n-                    return f\"API请求失败 (状态码: {response.status_code}): {error_message}\"\n-                except:\n-                    return f\"API请求失败 (状态码: {response.status_code}): {response.text[:200]}...\"\n-            \n-            # 解析响应\n-            try:\n-                response_data = response.json()\n-            except json.JSONDecodeError:\n-                return f\"无法解析API响应: {response.text[:200]}...\"\n-            \n-            # 提取回答结果\n-            if \"choices\" in response_data and len(response_data[\"choices\"]) > 0:\n-                message = response_data[\"choices\"][0].get(\"message\", {})\n-                content = message.get(\"content\", \"未能获取回答结果\")\n-                return content\n-            else:\n-                return f\"未能获取回答结果，API响应格式不符合预期: {json.dumps(response_data)[:200]}...\"\n-            \n-        except requests.exceptions.RequestException as e:\n-            return f\"API请求错误: {str(e)}\"\n-        except Exception as e:\n-            import traceback\n-            error_details = traceback.format_exc()\n             return f\"调用API时出错: {str(e)}\\n详细错误信息: {error_details}\" \n\\ No newline at end of file\n"
                },
                {
                    "date": 1741529102551,
                    "content": "Index: \n===================================================================\n--- \n+++ \n@@ -23,17 +23,17 @@\n     name = \"llm\"\n     description = \"使用大模型分析文档内容或回答问题，支持PDF、Word等格式\"\n     input_schema = {\n         \"type\": \"object\",\n-        \"required\": [\"file_path\", \"question\"],\n+        \"required\": [\"question\"],\n         \"properties\": {\n             \"file_path\": {\n                 \"type\": \"string\",\n-                \"description\": \"文档的本地路径或URL，例如'/path/to/document.pdf'或'https://example.com/document.pdf'\",\n+                \"description\": \"文档的本地路径或URL，例如'/path/to/document.pdf'或'https://example.com/document.pdf'（可选，如果只是提问则不需要）\",\n             },\n             \"question\": {\n                 \"type\": \"string\",\n-                \"description\": \"关于文档的问题，例如'文档里说了什么？'\",\n+                \"description\": \"关于文档的问题或直接提问，例如'文档里说了什么？'或'什么是人工智能？'\",\n             },\n             \"model\": {\n                 \"type\": \"string\",\n                 \"description\": \"使用的模型名称，默认为'glm-4'\",\n@@ -47,64 +47,89 @@\n     DEFAULT_MODEL = \"glm-4\"\n     \n     async def execute(self, arguments: Dict[str, Any]) -> List[types.TextContent | types.ImageContent | types.EmbeddedResource]:\n         \"\"\"\n-        分析文档内容\n+        分析文档内容或回答问题\n         \n         Args:\n-            arguments: 参数字典，必须包含'file_path'和'question'键\n+            arguments: 参数字典，可以包含'file_path'和'question'键\n             \n         Returns:\n             分析结果\n         \"\"\"\n         # 参数验证\n-        if \"file_path\" not in arguments:\n-            return [types.TextContent(\n-                type=\"text\",\n-                text=\"错误: 缺少必要参数 'file_path'\"\n-            )]\n-        \n         if \"question\" not in arguments:\n             return [types.TextContent(\n                 type=\"text\",\n                 text=\"错误: 缺少必要参数 'question'\"\n             )]\n         \n-        file_path = arguments[\"file_path\"]\n         question = arguments[\"question\"]\n         model = arguments.get(\"model\", self.DEFAULT_MODEL)\n         \n-        # 检查文件是否存在\n-        if not os.path.exists(file_path):\n-            return [types.TextContent(\n-                type=\"text\",\n-                text=f\"错误: 文件不存在: {file_path}\\n请检查路径是否正确，并确保文件可访问。\"\n-            )]\n-        \n-        try:\n-            # 添加处理信息\n-            results = [types.TextContent(\n-                type=\"text\",\n-                text=f\"# 文档分析\\n\\n正在分析文件: {os.path.basename(file_path)}\\n问题: {question}\\n\\n请稍候...\"\n-            )]\n+        # 检查是否提供了文件路径\n+        if \"file_path\" in arguments:\n+            file_path = arguments[\"file_path\"]\n+            # 检查文件路径是否是URL\n+            is_url = file_path.startswith(('http://', 'https://'))\n             \n-            # 调用外部API分析文档\n-            analysis_result = await self._analyze_document(file_path, question, model)\n+            # 如果不是URL，检查文件是否存在\n+            if not is_url and not os.path.exists(file_path):\n+                return [types.TextContent(\n+                    type=\"text\",\n+                    text=f\"错误: 文件不存在: {file_path}\\n请检查路径是否正确，并确保文件可访问。\\n如果使用Docker，请确保文件路径在容器内可访问，例如使用挂载的目录。\"\n+                )]\n             \n-            # 添加分析结果\n-            results.append(types.TextContent(\n-                type=\"text\",\n-                text=f\"## 分析结果\\n\\n{analysis_result}\"\n-            ))\n-            \n-            return results\n-        except Exception as e:\n-            import traceback\n-            error_details = traceback.format_exc()\n-            return [types.TextContent(\n-                type=\"text\",\n-                text=f\"错误: 分析文档失败: {str(e)}\\n详细错误信息: {error_details}\"\n-            )]\n+            try:\n+                # 添加处理信息\n+                results = [types.TextContent(\n+                    type=\"text\",\n+                    text=f\"# 文档分析\\n\\n正在分析文件: {os.path.basename(file_path) if not is_url else file_path}\\n问题: {question}\\n\\n请稍候...\"\n+                )]\n+                \n+                # 调用外部API分析文档\n+                analysis_result = await self._analyze_document(file_path, question, model)\n+                \n+                # 添加分析结果\n+                results.append(types.TextContent(\n+                    type=\"text\",\n+                    text=f\"## 分析结果\\n\\n{analysis_result}\"\n+                ))\n+                \n+                return results\n+            except Exception as e:\n+                import traceback\n+                error_details = traceback.format_exc()\n+                return [types.TextContent(\n+                    type=\"text\",\n+                    text=f\"错误: 分析文档失败: {str(e)}\\n详细错误信息: {error_details}\"\n+                )]\n+        else:\n+            # 直接回答问题，不分析文档\n+            try:\n+                # 添加处理信息\n+                results = [types.TextContent(\n+                    type=\"text\",\n+                    text=f\"# 问题\\n\\n{question}\\n\\n请稍候...\"\n+                )]\n+                \n+                # 调用外部API回答问题\n+                answer = await self._ask_question(question, model)\n+                \n+                # 添加回答结果\n+                results.append(types.TextContent(\n+                    type=\"text\",\n+                    text=f\"## 回答\\n\\n{answer}\"\n+                ))\n+                \n+                return results\n+            except Exception as e:\n+                import traceback\n+                error_details = traceback.format_exc()\n+                return [types.TextContent(\n+                    type=\"text\",\n+                    text=f\"错误: 回答问题失败: {str(e)}\\n详细错误信息: {error_details}\"\n+                )]\n     \n     async def _analyze_document(self, file_path: str, question: str, model: str) -> str:\n         \"\"\"\n         调用外部API分析文档\n@@ -195,5 +220,72 @@\n             return f\"API请求错误: {str(e)}\"\n         except Exception as e:\n             import traceback\n             error_details = traceback.format_exc()\n+            return f\"调用API时出错: {str(e)}\\n详细错误信息: {error_details}\"\n+    \n+    async def _ask_question(self, question: str, model: str) -> str:\n+        \"\"\"\n+        调用外部API回答问题\n+        \n+        Args:\n+            question: 问题\n+            model: 模型名称\n+            \n+        Returns:\n+            回答结果\n+        \"\"\"\n+        try:\n+            # 使用requests库直接发送请求\n+            url = f\"{self.BASE_URL}/v1/chat/completions\"\n+            \n+            # 构建请求数据\n+            payload = {\n+                \"model\": model,\n+                \"messages\": [\n+                    {\n+                        \"role\": \"user\",\n+                        \"content\": question\n+                    }\n+                ],\n+                \"stream\": False\n+            }\n+            \n+            # 设置请求头\n+            headers = {\n+                'Accept': 'application/json',\n+                'Authorization': f'Bearer {self.API_KEY}',\n+                'Content-Type': 'application/json'\n+            }\n+            \n+            # 发送请求\n+            response = requests.post(url, json=payload, headers=headers, timeout=60)\n+            \n+            # 检查响应状态码\n+            if response.status_code != 200:\n+                try:\n+                    error_data = response.json()\n+                    error_message = error_data.get(\"message\", \"未知错误\")\n+                    return f\"API请求失败 (状态码: {response.status_code}): {error_message}\"\n+                except:\n+                    return f\"API请求失败 (状态码: {response.status_code}): {response.text[:200]}...\"\n+            \n+            # 解析响应\n+            try:\n+                response_data = response.json()\n+            except json.JSONDecodeError:\n+                return f\"无法解析API响应: {response.text[:200]}...\"\n+            \n+            # 提取回答结果\n+            if \"choices\" in response_data and len(response_data[\"choices\"]) > 0:\n+                message = response_data[\"choices\"][0].get(\"message\", {})\n+                content = message.get(\"content\", \"未能获取回答结果\")\n+                return content\n+            else:\n+                return f\"未能获取回答结果，API响应格式不符合预期: {json.dumps(response_data)[:200]}...\"\n+            \n+        except requests.exceptions.RequestException as e:\n+            return f\"API请求错误: {str(e)}\"\n+        except Exception as e:\n+            import traceback\n+            error_details = traceback.format_exc()\n             return f\"调用API时出错: {str(e)}\\n详细错误信息: {error_details}\" \n\\ No newline at end of file\n"
                }
            ],
            "date": 1741527987944,
            "name": "Commit-0",
            "content": "\"\"\"\n大语言模型交互工具，使用外部大模型API分析文档内容或回答问题\n\"\"\"\n\nimport os\nimport json\nimport http.client\nimport urllib.parse\nimport base64\nimport mimetypes\nimport uuid\nimport requests\nfrom typing import Dict, List, Any, Optional, Tuple\nimport mcp.types as types\nfrom . import BaseTool, ToolRegistry\n\n@ToolRegistry.register\nclass LLMTool(BaseTool):\n    \"\"\"\n    大语言模型交互工具，使用外部大模型API分析文档内容或回答问题\n    \"\"\"\n    \n    name = \"llm\"\n    description = \"使用大模型分析文档内容或回答问题，支持PDF、Word等格式\"\n    input_schema = {\n        \"type\": \"object\",\n        \"required\": [\"question\"],\n        \"properties\": {\n            \"file_path\": {\n                \"type\": \"string\",\n                \"description\": \"文档的本地路径或URL，例如'/path/to/document.pdf'或'https://example.com/document.pdf'\",\n            },\n            \"question\": {\n                \"type\": \"string\",\n                \"description\": \"关于文档的问题或直接提问，例如'文档里说了什么？'或'什么是人工智能？'\",\n            },\n            \"model\": {\n                \"type\": \"string\",\n                \"description\": \"使用的模型名称，默认为'glm-4'\",\n            }\n        },\n    }\n    \n    # API配置\n    BASE_URL = \"http://host.docker.internal:8009\"\n    API_KEY = \"eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJzdWIiOiJhMjI3ZDkwYjVmMTY0MzcxYTcxMTdlMmRiODRjYzJiNSIsImV4cCI6MTc1NjYyMTg3NCwibmJmIjoxNzQxMDY5ODc0LCJpYXQiOjE3NDEwNjk4NzQsImp0aSI6IjhhZjA4YTU4YjIyNTQzODY5MDdkNGNlNTcwMzFjNmQ5IiwidWlkIjoiNjZkMDQ2NzdkM2IzNmZlYmU1ODFkMWZkIiwidHlwZSI6InJlZnJlc2gifQ.cAOioruMC0jLneIcbSyhuXJ9JIx_Bz79ndTTxlsWe5I\"\n    DEFAULT_MODEL = \"glm-4\"\n    \n    async def execute(self, arguments: Dict[str, Any]) -> List[types.TextContent | types.ImageContent | types.EmbeddedResource]:\n        \"\"\"\n        分析文档内容\n        \n        Args:\n            arguments: 参数字典，必须包含'file_path'和'question'键\n            \n        Returns:\n            分析结果\n        \"\"\"\n        # 参数验证\n        if \"file_path\" not in arguments:\n            return [types.TextContent(\n                type=\"text\",\n                text=\"错误: 缺少必要参数 'file_path'\"\n            )]\n        \n        if \"question\" not in arguments:\n            return [types.TextContent(\n                type=\"text\",\n                text=\"错误: 缺少必要参数 'question'\"\n            )]\n        \n        file_path = arguments[\"file_path\"]\n        question = arguments[\"question\"]\n        model = arguments.get(\"model\", self.DEFAULT_MODEL)\n        \n        # 检查文件是否存在\n        if not os.path.exists(file_path):\n            return [types.TextContent(\n                type=\"text\",\n                text=f\"错误: 文件不存在: {file_path}\\n请检查路径是否正确，并确保文件可访问。\"\n            )]\n        \n        try:\n            # 添加处理信息\n            results = [types.TextContent(\n                type=\"text\",\n                text=f\"# 文档分析\\n\\n正在分析文件: {os.path.basename(file_path)}\\n问题: {question}\\n\\n请稍候...\"\n            )]\n            \n            # 调用外部API分析文档\n            analysis_result = await self._analyze_document(file_path, question, model)\n            \n            # 添加分析结果\n            results.append(types.TextContent(\n                type=\"text\",\n                text=f\"## 分析结果\\n\\n{analysis_result}\"\n            ))\n            \n            return results\n        except Exception as e:\n            import traceback\n            error_details = traceback.format_exc()\n            return [types.TextContent(\n                type=\"text\",\n                text=f\"错误: 分析文档失败: {str(e)}\\n详细错误信息: {error_details}\"\n            )]\n    \n    async def _analyze_document(self, file_path: str, question: str, model: str) -> str:\n        \"\"\"\n        调用外部API分析文档\n        \n        Args:\n            file_path: 文档路径\n            question: 问题\n            model: 模型名称\n            \n        Returns:\n            分析结果\n        \"\"\"\n        try:\n            # 检查文件路径是否是URL\n            is_url = file_path.startswith(('http://', 'https://'))\n            \n            # 如果不是URL，检查文件是否存在\n            if not is_url and not os.path.exists(file_path):\n                return f\"错误: 文件不存在: {file_path}\\n请检查路径是否正确，并确保文件可访问。\"\n            \n            # 使用requests库直接发送请求\n            url = f\"{self.BASE_URL}/v1/chat/completions\"\n            \n            # 构建请求数据\n            payload = {\n                \"model\": model,\n                \"messages\": [\n                    {\n                        \"role\": \"user\",\n                        \"content\": [\n                            {\n                                \"type\": \"text\",\n                                \"text\": question\n                            }\n                        ]\n                    }\n                ],\n                \"stream\": False\n            }\n            \n            # 如果是URL，添加到消息中\n            if is_url:\n                payload[\"messages\"][0][\"content\"].insert(0, {\n                    \"type\": \"file\",\n                    \"file_url\": {\n                        \"url\": file_path\n                    }\n                })\n            else:\n                # 如果是本地文件，使用文本方式提问\n                file_name = os.path.basename(file_path)\n                payload[\"messages\"][0][\"content\"][0][\"text\"] = f\"我正在分析一个名为'{file_name}'的文档。请回答以下问题：{question}\"\n            \n            # 设置请求头\n            headers = {\n                'Accept': 'application/json',\n                'Authorization': f'Bearer {self.API_KEY}',\n                'Content-Type': 'application/json'\n            }\n            \n            # 发送请求\n            response = requests.post(url, json=payload, headers=headers, timeout=60)\n            \n            # 检查响应状态码\n            if response.status_code != 200:\n                try:\n                    error_data = response.json()\n                    error_message = error_data.get(\"message\", \"未知错误\")\n                    return f\"API请求失败 (状态码: {response.status_code}): {error_message}\"\n                except:\n                    return f\"API请求失败 (状态码: {response.status_code}): {response.text[:200]}...\"\n            \n            # 解析响应\n            try:\n                response_data = response.json()\n            except json.JSONDecodeError:\n                return f\"无法解析API响应: {response.text[:200]}...\"\n            \n            # 提取分析结果\n            if \"choices\" in response_data and len(response_data[\"choices\"]) > 0:\n                message = response_data[\"choices\"][0].get(\"message\", {})\n                content = message.get(\"content\", \"未能获取分析结果\")\n                return content\n            else:\n                return f\"未能获取分析结果，API响应格式不符合预期: {json.dumps(response_data)[:200]}...\"\n            \n        except requests.exceptions.RequestException as e:\n            return f\"API请求错误: {str(e)}\"\n        except Exception as e:\n            import traceback\n            error_details = traceback.format_exc()\n            return f\"调用API时出错: {str(e)}\\n详细错误信息: {error_details}\" "
        }
    ]
}