Medical Calculator MCP Service

import asyncio import json import sys import os from fastmcp import Client sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__)))) from config import MCP_SERVER_URL async def test_framingham_calculator(client): """测试 Framingham 风险评分计算器的各种功能""" def print_header(): print("\n" + "=" * 60) print("Framingham 风险评分计算器测试套件") print("=" * 60) def print_test_case(i, test_case): print(f"\n测试 {i:2d} | {test_case['name']}") print(f"- {test_case['description']}") print(f"- 输入参数: {test_case['params']}") def print_validation_result(expected, actual, errors=None, warnings=None): if expected == actual: status = "✅ 通过" else: status = "❌ 失败" expected_text = "有效" if expected else "无效" actual_text = "有效" if actual else "无效" print(f"- 验证结果: {status} (期望: {expected_text}, 实际: {actual_text})") if errors: print(f"- ⚠️ 错误: {errors}") if warnings: print(f"- ⚠️ 警告: {warnings}") def print_calculation_result(data): """打印完整的计算结果""" risk_percentage = data.get("value", "N/A") unit = data.get("unit", "") explanation = data.get("explanation", "") metadata = data.get("metadata", {}) warnings = data.get("warnings", []) # 基本结果 print(f"- 风险值: {risk_percentage}{unit}") # 元数据信息 if metadata: risk_score = metadata.get("risk_score") risk_category = metadata.get("risk_category") recommendation = metadata.get("recommendation") risk_factors = metadata.get("risk_factors", {}) if risk_score is not None: print(f"- 风险评分: {risk_score}") if risk_category: print(f"- 风险分类: {risk_category}") if recommendation: print(f"- 建议: {recommendation}") # 显示风险因素 if risk_factors: print("- 风险因素:") for factor, value in risk_factors.items(): print(f" • {factor}: {value}") # 警告信息 if warnings: for warning in warnings: print(f"- ⚠️ 警告: {warning}") # 解释（截取前几行显示） if explanation: lines = explanation.split('\n')[:3] print(f"- 解释摘要: {' '.join(lines).strip()}") def print_test_result(i, passed): if passed: status = "✅ 通过" else: status = "❌ 失败" print(f"- 测试结果: {status}") print("-" * 60) def print_summary(total, passed, failed): print(f"\n测试总结:") print(f" 总测试数: {total}") print(f" 通过数: {passed}") print(f" 失败数: {failed}") print(f" 成功率: {(passed/total*100):.1f}%") if failed == 0: print("\n✅ 所有测试都通过了！Framingham 计算器工作正常。") else: print(f"\n❌ {failed} 个测试失败，请检查实现。") print("\n测试覆盖范围:") features = [ "男性和女性计算", "多种年龄范围", "不同胆固醇水平", "血压和用药状态", "吸烟和糖尿病状态", "参数验证", "边界测试", "风险分层", ] for feature in features: print(f" - {feature}") # Test statistics total_tests = 0 passed_tests = 0 # Test cases test_cases = [ { "name": "低风险男性", "params": { "age": 45, "sex": "Male", "total_cholesterol": 180, "hdl_cholesterol": 50, "systolic_bp": 120, "bp_medication": False, "smoker": False, "diabetes": False }, "expected_valid": True, "description": "45岁男性，胆固醇正常，无其他危险因素", }, { "name": "高风险女性", "params": { "age": 65, "sex": "Female", "total_cholesterol": 280, "hdl_cholesterol": 35, "systolic_bp": 160, "bp_medication": True, "smoker": True, "diabetes": True }, "expected_valid": True, "description": "65岁女性，多个危险因素", }, { "name": "中等风险男性", "params": { "age": 55, "sex": "Male", "total_cholesterol": 220, "hdl_cholesterol": 40, "systolic_bp": 140, "bp_medication": False, "smoker": True, "diabetes": False }, "expected_valid": True, "description": "55岁男性，吸烟者，胆固醇偏高", }, { "name": "年轻女性", "params": { "age": 35, "sex": "Female", "total_cholesterol": 160, "hdl_cholesterol": 60, "systolic_bp": 110, "bp_medication": False, "smoker": False, "diabetes": False }, "expected_valid": True, "description": "35岁女性，低风险", }, { "name": "边界年龄（最小）", "params": { "age": 30, "sex": "Male", "total_cholesterol": 200, "hdl_cholesterol": 45, "systolic_bp": 130, "bp_medication": False, "smoker": False, "diabetes": False }, "expected_valid": True, "description": "30岁男性（最小年龄）", }, { "name": "边界年龄（最大）", "params": { "age": 79, "sex": "Female", "total_cholesterol": 250, "hdl_cholesterol": 40, "systolic_bp": 150, "bp_medication": True, "smoker": False, "diabetes": True }, "expected_valid": True, "description": "79岁女性（最大年龄）", }, { "name": "无效年龄（太小）", "params": { "age": 25, "sex": "Male", "total_cholesterol": 200, "hdl_cholesterol": 45, "systolic_bp": 130, "bp_medication": False, "smoker": False, "diabetes": False }, "expected_valid": False, "description": "25岁（年龄过小）", }, { "name": "无效年龄（太大）", "params": { "age": 85, "sex": "Male", "total_cholesterol": 200, "hdl_cholesterol": 45, "systolic_bp": 130, "bp_medication": False, "smoker": False, "diabetes": False }, "expected_valid": False, "description": "85岁（年龄过大）", }, { "name": "无效胆固醇（过低）", "params": { "age": 50, "sex": "Male", "total_cholesterol": 80, "hdl_cholesterol": 45, "systolic_bp": 130, "bp_medication": False, "smoker": False, "diabetes": False }, "expected_valid": False, "description": "总胆固醇过低（80 mg/dL）", }, { "name": "无效血压（过低）", "params": { "age": 50, "sex": "Male", "total_cholesterol": 200, "hdl_cholesterol": 45, "systolic_bp": 80, "bp_medication": False, "smoker": False, "diabetes": False }, "expected_valid": False, "description": "收缩压过低（80 mmHg）", }, ] print_header() # Execute test cases for i, test_case in enumerate(test_cases, 1): total_tests += 1 test_passed = True print_test_case(i, test_case) # Calculation test try: calc_result = await client.call_tool( "calculate", { "calculator_id": 46, "parameters": test_case["params"], }, ) # 使用 structured_content 或 data 属性获取实际数据 calc_data = calc_result.structured_content or calc_result.data or {} if isinstance(calc_data, dict) and calc_data.get("success") and "result" in calc_data: # 成功计算 data = calc_data["result"] print_calculation_result(data) # 检查是否符合预期 if not test_case["expected_valid"]: print("- 错误: 预期失败但计算成功") test_passed = False else: # 计算失败（可能是参数验证失败） error_msg = calc_data.get("error", "未知错误") if isinstance(calc_data, dict) else str(calc_data) print(f"- 计算失败: {error_msg}") # 检查是否符合预期 if test_case["expected_valid"]: print("- 错误: 预期成功但计算失败") test_passed = False except Exception as e: print(f"- 计算错误: {e}") # 检查是否符合预期 if test_case["expected_valid"]: test_passed = False # Update statistics if test_passed: passed_tests += 1 print_test_result(i, test_passed) print_summary(total_tests, passed_tests, total_tests - passed_tests) return passed_tests, total_tests - passed_tests async def main(): def print_header(): print("Framingham 风险评分计算器 MCP 测试") print("=" * 60) def print_connection_status(success, error=None): if success: print("✅ 成功连接到 MCP 服务器") else: print(f"❌ 连接失败: {error}") def print_overall_results(total_passed, total_failed): total_tests = total_passed + total_failed if total_tests == 0: return print("\n" + "=" * 60) print("Framingham 计算器测试结果") print("=" * 60) print(f"总测试数: {total_tests}") print(f"通过数: {total_passed}") print(f"失败数: {total_failed}") print(f"成功率: {(total_passed/total_tests*100):.1f}%") if total_failed == 0: print("\n✅ Framingham 计算器所有测试都通过了！") else: print(f"\n❌ {total_failed} 个测试失败，请检查 Framingham 计算器实现。") print_header() try: async with Client(MCP_SERVER_URL) as client: print_connection_status(True) passed, failed = await test_framingham_calculator(client) print_overall_results(passed, failed) except Exception as e: print_connection_status(False, str(e)) import traceback traceback.print_exc() return print("\n" + "=" * 60) print("✅ Framingham 计算器测试完成") if __name__ == "__main__": asyncio.run(main())