集成

compliance-mcp-agent/servers/APICallerServer.py

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+import requests
+import uvicorn
+from mcp.server.fastmcp.server import FastMCP
+from typing import Optional
+import logging
+
+# 配置日志记录
+logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
+
+# 1. 使用 FastMCP 创建一个 Server 实例
+mcp = FastMCP(
+    "APICallerServer",
+    title="API Caller Server",
+    description="A server that provides a tool to call APIs.",
+    version="0.2.0"  # a new version
+)
+
+# 2. 使用 @mcp.tool() 装饰器来定义一个工具
+@mcp.tool()
+def api_caller(method: str, url: str, headers: Optional[dict] = None, params: Optional[dict] = None, json_body: Optional[dict] = None) -> dict:
+    """
+    一个通用的API调用工具，可以发送HTTP请求。
+    """
+    logging.info(f"api_caller: Received request -> method={method}, url={url}, params={params}, json_body={json_body}")
+    try:
+        response = requests.request(
+            method=method,
+            url=url,
+            headers=headers,
+            params=params,
+            json=json_body
+        )
+        response.raise_for_status()  # 如果状态码是 4xx 或 5xx，则引发HTTPError
+
+        logging.info(f"api_caller: Request to {url} successful with status code {response.status_code}")
+
+        # 尝试将响应解析为JSON，如果失败则作为纯文本返回
+        try:
+            response_body = response.json()
+        except requests.exceptions.JSONDecodeError:
+            response_body = response.text
+        
+        return {
+            "status_code": response.status_code,
+            "headers": dict(response.headers),
+            "body": response_body
+        }
+    except requests.exceptions.RequestException as e:
+        logging.error(f"api_caller: Request to {url} failed. Error: {e}", exc_info=True)
+        return {
+            "error": "APIRequestError",
+            "message": str(e)
+        }
+
+# 3. (可选) 如果直接运行此文件，则启动服务器
+if __name__ == "__main__":
+    # FastMCP对象本身不是ASGI应用，但它的 streamable_http_app() 方法会返回一个
+    uvicorn.run(mcp.streamable_http_app(), host="127.0.0.1", port=8001) 

compliance-mcp-agent/servers/DMSProviderServer.py

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+import requests
+import uvicorn
+import logging
+from mcp.server.fastmcp import FastMCP
+from typing import List, Dict, Any
+import json
+import os
+
+# --- 配置 ---
+logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
+
+# --- MCP Server 定义 ---
+mcp = FastMCP()
+
+# 定义正确的DMS服务器基地址
+DMS_BASE_URL = "http://127.0.0.1:5001"
+MOCK_DMS_API_LIST_URL = f"{DMS_BASE_URL}/api/schema/manage/schema"
+MOCK_DMS_SCHEMA_DETAIL_URL_TEMPLATE = f"{DMS_BASE_URL}/api/schema/manage/schema/{{model_id}}"
+
+@mcp.tool()
+def get_api_list() -> Dict[str, Any]:
+    """
+    通过HTTP请求从模拟的DMS服务器获取所有可用API的列表,并返回一个干净的、包含records的字典。
+    """
+    logging.info(f"DMSProviderServer: Attempting to fetch API list from {MOCK_DMS_API_LIST_URL}")
+    try:
+        response = requests.get(MOCK_DMS_API_LIST_URL, timeout=5)
+        response.raise_for_status()
+        
+        raw_data = response.json()
+        
+        # 核心修正：根据mock server的实际返回，深入到'data'键下提取'records'
+        records = raw_data.get("data", {}).get("records", [])
+        
+        logging.info(f"DMSProviderServer: Successfully parsed response. Found {len(records)} records.")
+        
+        # 返回一个干净、统一的结构，并加入版本号探针
+        return {"records": records, "version": "1.1"}
+
+    except requests.exceptions.RequestException as e:
+        error_message = f"Failed to connect to mock DMS server for API list: {e}"
+        logging.error(f"DMSProviderServer: {error_message}")
+        return {"records": [], "error": error_message}
+    except json.JSONDecodeError:
+        error_message = "Failed to decode JSON response for API list from mock DMS server."
+        logging.error(f"DMSProviderServer: {error_message}")
+        return {"records": [], "error": error_message}
+
+@mcp.tool()
+def get_schema_by_id(model_id: str) -> Dict[str, Any]:
+    """
+    根据模型ID，通过HTTP请求从模拟的DMS服务器获取其JSON Schema。
+    """
+    schema_url = MOCK_DMS_SCHEMA_DETAIL_URL_TEMPLATE.format(model_id=model_id)
+    logging.info(f"DMSProviderServer: Attempting to fetch schema for '{model_id}' from {schema_url}")
+    
+    try:
+        response = requests.get(schema_url, timeout=5)
+        response.raise_for_status()
+        
+        raw_data = response.json()
+        
+        # 核心修正：提取'data'键下的schema对象
+        schema = raw_data.get("data")
+        
+        if schema:
+            logging.info(f"DMSProviderServer: Successfully parsed schema for '{model_id}'.")
+            return {"schema": schema}
+        else:
+            error_message = f"Schema data for '{model_id}' is empty or missing in the response."
+            logging.warning(f"DMSProviderServer: {error_message}")
+            return {"error": error_message}
+
+    except requests.exceptions.RequestException as e:
+        error_message = f"Failed to connect to mock DMS server for schema '{model_id}': {e}"
+        logging.error(f"DMSProviderServer: {error_message}")
+        return {"error": error_message}
+    except json.JSONDecodeError:
+        error_message = f"Failed to decode JSON response for schema '{model_id}' from mock DMS server."
+        logging.error(f"DMSProviderServer: {error_message}")
+        return {"error": error_message}
+
+
+@mcp.tool()
+def get_dms_crud_endpoints(model_id: str) -> Dict[str, Any]:
+    """
+    根据模型ID，生成并返回其所有标准的CRUD操作端点（create, list, read, update, delete）的完整定义。
+    """
+    # 这个函数的逻辑是基于名称生成，暂时不需要对接mock服务，所以保持不变
+    base_path = model_id.split('.')[0]
+    
+    endpoints = {
+        "create": {
+            "method": "POST",
+            "url": f"{DMS_BASE_URL}/api/dms/wb_ml/v1/{base_path}"
+        },
+        "list": {
+            "method": "POST", # 根据mock server，list是POST
+            "url": f"{DMS_BASE_URL}/api/dms/wb_ml/v1/{base_path}/1.0.0" # 根据mock server，需要版本号
+        },
+        "read": {
+            "method": "GET",
+            "url": f"{DMS_BASE_URL}/api/dms/wb_ml/v1/{base_path}/1.0.0/{{id}}" # 根据mock server，需要版本号
+        },
+        "update": {
+            "method": "PUT",
+            "url": f"{DMS_BASE_URL}/api/dms/wb_ml/v1/{base_path}"
+        },
+        "delete": {
+            "method": "DELETE",
+            "url": f"{DMS_BASE_URL}/api/dms/wb_ml/v1/{base_path}"
+        }
+    }
+    return endpoints
+
+# --- 启动服务器 ---
+if __name__ == "__main__":
+    import uvicorn
+    uvicorn.run(mcp.streamable_http_app(), host="127.0.0.1", port=8003) 

compliance-mcp-agent/servers/SchemaValidatorServer.py

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+from mcp.server.fastmcp.server import FastMCP
+from pydantic import BaseModel, ValidationError
+import jsonschema
+import uvicorn
+import logging
+from jsonschema import validate, ValidationError
+from mcp.server.fastmcp.server import FastMCP
+
+# 新增导入
+from response_utils import extract_data_for_validation
+
+mcp = FastMCP(
+    "SchemaValidatorServer",
+    title="JSON Schema Validator Server",
+    description="A server that provides a tool to validate data against a JSON Schema.",
+    version="0.1.0"
+)
+
+@mcp.tool()
+def validate_schema(data_instance: dict, schema: dict) -> dict:
+    """
+    Validates a data instance against a given JSON Schema.
+
+    Args:
+        data_instance: The data object to validate.
+        schema: The JSON Schema to validate against.
+
+    Returns:
+        A dictionary containing the validation result.
+        {"isValid": True} on success.
+        {"isValid": False, "error": "Validation error message"} on failure.
+    """
+    try:
+        jsonschema.validate(instance=data_instance, schema=schema)
+        return {"isValid": True, "error": None}
+    except ValidationError as e:
+        logging.error(f"SchemaValidator: Validation failed. Error: {e.message}", exc_info=True)
+        return {"isValid": False, "error": e.message}
+    except Exception as e:
+        # Catch other potential errors from the jsonschema library
+        return {"isValid": False, "error": str(e)}
+
+@mcp.tool()
+def validate_flexible_schema(api_response: dict, item_schema: dict) -> dict:
+    """
+    对一个可能带有标准包装（如 {code, message, data}）的API响应进行灵活的schema验证。
+    它能自动提取核心业务数据（无论是单个对象还是列表）并逐项进行验证。
+
+    Args:
+        api_response (dict): 完整的API响应体。
+        item_schema (dict): 描述核心业务数据**单个元素**的JSON Schema。
+    
+    Returns:
+        dict: 一个包含验证结果的字典, {"isValid": True} 或 {"isValid": False, "error": "..."}。
+    """
+    logging.info("SchemaValidator: Running flexible validation...")
+    try:
+        # 使用工具函数提取需要验证的数据
+        items_to_validate = extract_data_for_validation(api_response)
+
+        if not items_to_validate:
+            error_message = "Flexible validation failed: Could not extract any items to validate from the response."
+            logging.warning(error_message)
+            return {"isValid": False, "error": error_message}
+        
+        logging.info(f"Flexible validation: Extracted {len(items_to_validate)} item(s) to validate.")
+
+        # 逐个验证提取出的项
+        for i, item in enumerate(items_to_validate):
+            validate(instance=item, schema=item_schema)
+            logging.info(f"  -> Item {i+1}/{len(items_to_validate)} passed validation.")
+
+        logging.info("SchemaValidator: Flexible validation successful. All items conform to the schema.")
+        return {"isValid": True}
+
+    except ValidationError as e:
+        error_message = f"Flexible validation failed on an item. Error: {e.message}"
+        logging.error(error_message, exc_info=True)
+        return {"isValid": False, "error": error_message}
+    except Exception as e:
+        error_message = f"An unexpected error occurred during flexible validation: {e}"
+        logging.error(error_message, exc_info=True)
+        return {"isValid": False, "error": error_message}
+
+
+# --- 启动服务器 ---
+if __name__ == "__main__":
+    uvicorn.run(mcp.streamable_http_app(), host="127.0.0.1", port=8002) 

compliance-mcp-agent/servers/TestManagerServer.py

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+import uvicorn
+import logging
+from mcp.server.fastmcp import FastMCP
+from typing import List, Dict, Any
+import threading
+
+# --- 配置 ---
+logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(name)s - %(levelname)s - %(message)s')
+logger = logging.getLogger(__name__)
+
+# --- MCP Server 定义 ---
+mcp = FastMCP()
+
+# 使用一个简单的字典和线程锁来安全地管理状态
+test_state: Dict[str, Any] = {}
+state_lock = threading.Lock()
+
+def _reset_state():
+    """重置测试状态，不加锁，供内部调用"""
+    global test_state
+    test_state = {
+        "results": {}, # e.g., {"api_id_1": [{"task_name": "...", "status": "...", "details": "..."}]}
+        "apis_pending_init": [],
+    }
+
+_reset_state() # 初始化状态
+
+@mcp.tool()
+def initialize_test_plan(api_ids: List[str]) -> Dict[str, Any]:
+    """
+    根据提供的API ID列表，初始化测试计划。
+    这会重置所有测试状态，并为每个API准备好存储多次任务结果的列表。
+    """
+    with state_lock:
+        _reset_state()
+        for api_id in api_ids:
+            test_state["results"][api_id] = []
+        
+        test_state["apis_pending_init"] = list(api_ids)
+        logger.info(f"TestManager: Initialized test plan with {len(api_ids)} APIs.")
+        return {
+            "status": "success",
+            "message": f"Test plan initialized for {len(api_ids)} APIs.",
+            "apis_pending": list(api_ids)
+        }
+
+@mcp.tool()
+def record_test_result(api_id: str, task_name: str, status: str, details: str) -> Dict[str, Any]:
+    """
+    记录一个API在一个特定任务上的测试结果。
+    
+    Args:
+        api_id (str): 被测试的API的ID。
+        task_name (str): 执行的任务的名称。
+        status (str): 测试状态，例如 'passed' 或 'failed'。
+        details (str): 关于测试结果的详细描述或摘要。
+    """
+    with state_lock:
+        if api_id not in test_state["results"]:
+            # 如果由于某种原因API ID不存在，先创建它
+            test_state["results"][api_id] = []
+        
+        # 将本次任务的结果追加到列表中
+        test_state["results"][api_id].append({
+            "task_name": task_name,
+            "status": status,
+            "details": details
+        })
+        
+        logger.info(f"TestManager: Recorded result for {api_id} on task '{task_name}': {status}")
+        return {"status": "success", "message": f"Result for {api_id} on task '{task_name}' recorded."}
+
+@mcp.tool()
+def get_test_summary() -> Dict[str, Any]:
+    """
+    获取整个测试活动的最终摘要。
+    """
+    with state_lock:
+        total_apis = len(test_state["results"])
+        tasks_completed_count = sum(len(tasks) for tasks in test_state["results"].values())
+
+        summary = {
+            "total_apis": total_apis,
+            "total_tasks_completed": tasks_completed_count,
+            "results": test_state["results"]
+        }
+        logger.info("TestManager: Providing test summary.")
+        return summary
+
+# --- 启动服务器 ---
+if __name__ == "__main__":
+    import uvicorn
+    
+    # 移除get_next_api_to_test工具，因为它在M*N模型中不再需要。
+    # 我们使用 try...except 来确保即使工具不存在或属性名不正确，程序也不会崩溃。
+    try:
+        # 基于之前的观察，我们尝试使用 _tools 属性
+        if "get_next_api_to_test" in mcp._tools:
+            del mcp._tools["get_next_api_to_test"]
+            logger.info("Successfully removed deprecated tool: get_next_api_to_test")
+    except (AttributeError, KeyError):
+        logger.warning("Could not remove 'get_next_api_to_test' tool (it may not exist or the tools attribute name is different). Continuing...")
+        pass
+
+    uvicorn.run(mcp.streamable_http_app(), host="127.0.0.1", port=8004) 

compliance-mcp-agent/servers/response_utils.py

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+from typing import Any, List, Optional
+import logging
+
+logger = logging.getLogger(__name__)
+
+def extract_data_for_validation(response_json: Any, nested_list_keywords: Optional[List[str]] = None) -> List[Any]:
+    """
+    从原始API响应JSON中智能提取需要被验证的核心业务数据列表。
+    即使只有一个对象，也返回一个单元素的列表。
+
+    策略:
+    1. 如果响应体是包含 'code' 和 'data' 的标准包装，则提取 'data' 的内容。
+    2. 对上一步的结果，遍历一个关键字列表(nested_list_keywords)，检查是否存在分页列表模式，如果存在则提取该列表。
+    3. 如果处理后的数据是列表，直接返回该列表。
+    4. 如果处理后的数据是单个对象（字典），将其包装在单元素列表中返回。
+    5. 如果数据为空或不适用，返回空列表。
+    """
+    if nested_list_keywords is None:
+        nested_list_keywords = ["list", "records", "items", "data"]
+
+    if not response_json:
+        return []
+
+    data_to_process = response_json
+
+    # 策略 1: 解开标准包装
+    if isinstance(response_json, dict) and 'code' in response_json and 'data' in response_json:
+        logger.debug("检测到标准响应包装，提取 'data' 字段内容进行处理。")
+        data_to_process = response_json['data']
+
+    # 策略 2: 提取嵌套的分页列表
+    if isinstance(data_to_process, dict):
+        for keyword in nested_list_keywords:
+            if keyword in data_to_process and isinstance(data_to_process[keyword], list):
+                logger.debug(f"检测到关键字为 '{keyword}' 的嵌套列表，提取其内容。")
+                data_to_process = data_to_process[keyword]
+                break # 找到第一个匹配的就停止
+
+    # 策略 3 & 4: 统一返回列表
+    if isinstance(data_to_process, list):
+        logger.debug(f"数据本身为列表，包含 {len(data_to_process)} 个元素，直接返回。")
+        return data_to_process
+    
+    if isinstance(data_to_process, dict):
+        logger.debug("数据为单个对象，将其包装在列表中返回。")
+        return [data_to_process]
+
+    # 策略 5: 对于其他情况（如数据为None或非对象/列表类型），返回空列表
+    logger.warning(f"待处理的数据既不是列表也不是对象，无法提取进行验证。数据: {str(data_to_process)[:100]}")
+    return []