基于新注册中心实现 AI 智能体的 MCP 工具智能发现架构设计与代码级落地实现 原创

class MCPToolDiscoveryEngine:
    def __init__(self):
        self.registry_url = "https://registry.modelcontextprotocol.org"
        self.local_cache = {}
        self.capability_index = CapabilityIndex()


    async def discover_tools_by_intent(self, user_intent: str):
        """根据用户意图动态发现相关工具"""


        # 1. 分析用户意图
        intent_analysis = await self.intent_analyzer.analyze(user_intent)


        # 2. 查询注册中心
        available_servers = await self.query_registry({
            'capabilities': intent_analysis.required_capabilities,
            'domain': intent_analysis.domain,
            'quality_threshold': 0.8
        })


        # 3. 评估工具品质
        qualified_tools = []
        for server in available_servers:
            quality_score = await self.evaluate_tool_quality(server)
            if quality_score > 0.8:
                qualified_tools.append({
                    'server': server,
                    'quality': quality_score,
                    'relevance': intent_analysis.calculate_relevance(server)
                })


        # 4. 排序并返回最佳工具组合
        return sorted(qualified_tools, 
                     key=lambda x: x['quality'] * x['relevance'], 
                     reverse=True)

class IntelligentCompositionEngine:
    def __init__(self):
        self.composition_patterns = CompositionPatternLibrary()
        self.execution_optimizer = ExecutionOptimizer()


    async def create_execution_plan(self, user_request: str, available_tools: list):
        """创建智能执行计划"""


        # 1. 分解复杂任务
        subtasks = await self.task_decomposer.decompose(user_request)


        # 2. 工具能力匹配
        tool_mapping = {}
        for subtask in subtasks:
            best_tools = await self.match_tools_to_task(subtask, available_tools)
            tool_mapping[subtask.id] = best_tools


        # 3. 组合模式识别
        composition_pattern = await self.identify_composition_pattern(
            subtasks, tool_mapping
        )


        # 4. 执行计划优化
        execution_plan = await self.execution_optimizer.optimize({
            'subtasks': subtasks,
            'tool_mapping': tool_mapping,
            'pattern': composition_pattern,
            'constraints': {
                'max_latency': 5000,  # 5秒
                'max_cost': 100,      # 100 token
                'parallel_limit': 5   # 最多5个平行任务
            }
        })


        return execution_plan

async def solar_panel_feasibility_analysis(user_request: str, user_location: str):
    """太阳能可行性分析的动态工具组合"""


    # 1. 动态发现相关工具
    discovered_tools = await discovery_engine.discover_tools_by_intent(
        "solar panel cost-benefit analysis"
    )


    # 2. AI 自主选择最佳工具组合
    selected_tools = {
        'weather_service': next(t for t in discovered_tools if 'weather' in t['capabilities']),
        'energy_calculator': next(t for t in discovered_tools if 'energy_calculation' in t['capabilities']),
        'cost_estimator': next(t for t in discovered_tools if 'cost_estimation' in t['capabilities']),
        'incentive_checker': next(t for t in discovered_tools if 'government_incentives' in t['capabilities']),
        'roi_analyzer': next(t for t in discovered_tools if 'roi_analysis' in t['capabilities'])
    }


    # 3. 智能执行编排
    results = {}


    # 并行获取基础数据
    async with TaskGroup() as tg:
        tg.create_task(get_solar_irradiance(user_location, selected_tools['weather_service']))
        tg.create_task(get_local_energy_rates(user_location, selected_tools['energy_calculator']))
        tg.create_task(get_installation_costs(user_location, selected_tools['cost_estimator']))
        tg.create_task(get_government_incentives(user_location, selected_tools['incentive_checker']))


    # 4. 综合分析
    roi_analysis = await selected_tools['roi_analyzer'].call_tool('calculate_roi', {
        'solar_data': results['solar_irradiance'],
        'energy_rates': results['energy_rates'],
        'installation_cost': results['installation_costs'],
        'incentives': results['incentives'],
        'analysis_period': 25  # 25年分析期
    })


    return {
        'feasible': roi_analysis['payback_period'] < 10,
        'estimated_savings': roi_analysis['total_savings'],
        'payback_period': roi_analysis['payback_period'],
        'environmental_impact': roi_analysis['co2_reduction'],
        'tool_chain_used': [tool['name'] for tool in selected_tools.values()]
    }

{
  "feasible": true,
  "estimated_savings": "NT$2,850,000",
  "payback_period": "7.2 years",
  "environmental_impact": "45 tons CO2 reduced over 25 years",
  "tool_chain_used": [
    "TaiwanWeatherAPI_v2.1",
    "TaipowerRateCalculator_v1.5", 
    "SolarCostEstimator_v3.0",
    "GovernmentIncentiveChecker_v2.3",
    "ROIAnalyzer_v1.8"
  ],
  "confidence_score": 0.89,
  "data_freshness": "2025-08-16T23:30:00Z"
}

class DynamicSupplyChainOptimizer:
    async def optimize_supply_chain(self, disruption_event: str):
        """动态供应链优化"""


        # 动态发现相关服务
        tools = await self.discover_tools([
            'logistics_tracking',
            'inventory_management', 
            'supplier_database',
            'weather_monitoring',
            'geopolitical_risk_assessment'
        ])


        # 智能组合分析
        optimization_plan = await self.compose_analysis({
            'event': disruption_event,
            'current_inventory': await tools['inventory'].get_current_stock(),
            'supplier_status': await tools['supplier'].check_all_suppliers(),
            'logistics_options': await tools['logistics'].get_alternative_routes(),
            'risk_factors': await tools['risk'].assess_current_risks()
        })


        return optimization_plan

class PortfolioOptimizationAgent:
    async def optimize_portfolio(self, client_profile: dict, market_conditions: str):
        """动态投资组合优化"""


        # 即时发现金融工具和数据源
        financial_tools = await self.discover_financial_tools([
            'market_data_provider',
            'risk_assessment_engine',
            'regulatory_compliance_checker',
            'esg_scoring_service',
            'tax_optimization_calculator'
        ])


        # 智能分析组合
        portfolio_recommendation = await self.intelligent_composition({
            'client_risk_tolerance': client_profile['risk_tolerance'],
            'investment_horizon': client_profile['investment_horizon'],
            'current_market_data': await financial_tools['market_data'].get_realtime_data(),
            'regulatory_constraints': await financial_tools['compliance'].check_regulations(client_profile),
            'esg_preferences': client_profile.get('esg_preferences', {}),
            'tax_situation': client_profile['tax_status']
        })


        return portfolio_recommendation

class ToolQualityAssessment:
    def __init__(self):
        self.quality_metrics = {
            'reliability': 0.3,      # 可靠性权重
            'performance': 0.25,     # 效能权重
            'security': 0.25,        # 安全性权重
            'documentation': 0.1,    # 文档品质权重
            'community_feedback': 0.1  # 社群反馈权重
        }


    async def evaluate_tool_quality(self, tool_server: str):
        """评估工具品质"""


        metrics = {}


        # 可靠性测试
        metrics['reliability'] = await self.test_reliability(tool_server)


        # 效能测试
        metrics['performance'] = await self.benchmark_performance(tool_server)


        # 安全性扫描
        metrics['security'] = await self.security_scan(tool_server)


        # 文档品质分析
        metrics['documentation'] = await self.analyze_documentation(tool_server)


        # 社群反馈分析
        metrics['community_feedback'] = await self.get_community_score(tool_server)


        # 计算综合分数
        quality_score = sum(
            metrics[metric] * weight 
            for metric, weight in self.quality_metrics.items()
        )


        return {
            'overall_score': quality_score,
            'detailed_metrics': metrics,
            'recommendation': self.get_recommendation(quality_score),
            'last_evaluated': datetime.now()
        }

class EnterpriseToolStrategy:
    def __init__(self):
        self.approved_tools = set()
        self.blacklisted_tools = set()
        self.approval_workflows = ApprovalWorkflows()


    async def evaluate_new_tool(self, tool_info: dict):
        """评估新工具是否符合企业策略"""


        evaluation = {
            'security_compliance': await self.check_security_compliance(tool_info),
            'data_governance': await self.check_data_governance(tool_info),
            'business_alignment': await self.assess_business_value(tool_info),
            'cost_benefit': await self.analyze_cost_benefit(tool_info),
            'integration_complexity': await self.assess_integration_effort(tool_info)
        }


        # 自动化决策
        if all(score > 0.7 for score in evaluation.values()):
            return await self.auto_approve_tool(tool_info)
        elif any(score < 0.3 for score in evaluation.values()):
            return await self.auto_reject_tool(tool_info, evaluation)
        else:
            return await self.request_manual_review(tool_info, evaluation)

class PredictiveToolCaching:
    def __init__(self):
        self.usage_patterns = UsagePatternAnalyzer()
        self.cache_manager = DistributedCacheManager()


    async def predict_and_preload_tools(self, user_context: dict):
        """预测并预加载可能需要的工具"""


        # 分析用户行为模式
        user_patterns = await self.usage_patterns.analyze_user_behavior(
            user_context['user_id']
        )


        # 预测可能需要的工具
        predicted_tools = await self.predict_required_tools({
            'current_time': datetime.now(),
            'user_patterns': user_patterns,
            'current_context': user_context,
            'seasonal_factors': await self.get_seasonal_factors()
        })


        # 预加载高概率工具
        for tool in predicted_tools:
            if tool['probability'] > 0.6:
                await self.cache_manager.preload_tool(tool['server_info'])

class DynamicLoadBalancer:
    async def route_tool_request(self, tool_request: dict):
        """动态路由工具请求到最佳服务器实例"""


        available_instances = await self.discover_tool_instances(
            tool_request['tool_type']
        )


        # 评估每个实例的当前状态
        instance_scores = []
        for instance in available_instances:
            score = await self.calculate_instance_score({
                'current_load': instance.current_load,
                'response_time': instance.avg_response_time,
                'error_rate': instance.error_rate,
                'geographic_proximity': self.calculate_distance(
                    tool_request['client_location'], 
                    instance.location
                )
            })
            instance_scores.append((instance, score))


        # 选择最佳实例
        best_instance = max(instance_scores, key=lambda x: x[1])[0]


        return await self.execute_request(best_instance, tool_request)

class DynamicAccessControl:
    async def authorize_tool_access(self, user_id: str, tool_info: dict, context: dict):
        """动态授权工具存取"""


        # 基础权限检查
        base_permissions = await self.get_user_permissions(user_id)


        # 上下文感知授权
        contextual_factors = {
            'time_of_day': datetime.now().hour,
            'request_location': context.get('location'),
            'request_urgency': context.get('urgency', 'normal'),
            'business_justification': context.get('justification'),
            'risk_level': await self.assess_request_risk(tool_info, context)
        }


        # 动态决策
        authorization_result = await self.dynamic_authorization_engine.decide({
            'user_permissions': base_permissions,
            'tool_requirements': tool_info['security_requirements'],
            'contextual_factors': contextual_factors,
            'policy_rules': await self.get_applicable_policies(user_id, tool_info)
        })


        return authorization_result

class EcosystemHealthMonitor:
    async def monitor_ecosystem_health(self):
        """监控整个工具生态系统的健康状况"""


        health_metrics = {
            'tool_availability': await self.check_tool_availability(),  # 工具可用率
            'response_times': await self.measure_response_times(),      # 响应时长
            'error_rates': await self.calculate_error_rates(),          # 错误率
            'user_satisfaction': await self.survey_user_satisfaction(),# 用户满意度
            'security_incidents': await self.count_security_incidents(),# 安全事件数
            'cost_efficiency': await self.analyze_cost_efficiency()     # 成本效益
        }


        # 异常检测：识别指标偏离正常范围的情况
        anomalies = await self.detect_anomalies(health_metrics)


        # 自动修复与告警：按异常严重程度分级处理
        for anomaly in anomalies:
            if anomaly['severity'] == 'high':  # 高严重级异常（如核心工具不可用、安全漏洞）
                await self.trigger_auto_remediation(anomaly)  # 触发自动修复流程
            else:  # 中低严重级异常（如响应延迟略高、个别用户反馈不佳）
                await self.alert_operations_team(anomaly)     # 通知运维团队介入


        # 返回生态健康报告
        return {
            'overall_health': self.calculate_overall_health(health_metrics),  # 整体健康评分
            'detailed_metrics': health_metrics,                              # 各维度详细数据
            'anomalies': anomalies,                                          # 已识别异常列表
            'recommendations': await self.generate_recommendations(health_metrics)  # 优化建议
        }

class CrossDomainIntelligence:
    async def fuse_domain_knowledge(self, request: str):
        """跨领域知识融合：整合多领域工具能力，解决复杂交叉需求"""


        # 第一步：识别需求涉及的领域（如“为慢性病患者设计保险+健康管理方案”涉及医疗+金融+生活服务）
        involved_domains = await self.identify_domains(request)


        # 第二步：为每个领域匹配专业工具（“医疗领域”匹配血糖监测、饮食建议工具，“金融领域”匹配保险推荐工具）
        domain_experts = {}
        for domain in involved_domains:
            domain_experts[domain] = await self.discover_domain_experts(domain)


        # 第三步：协调多领域工具协作，按“专业领域优先、跨领域共识互补”策略生成方案
        fusion_result = await self.orchestrate_cross_domain_collaboration({
            'request': request,
            'domain_experts': domain_experts,
            'fusion_strategy': 'consensus_with_specialization'  # 专业领域输出核心结论，跨领域工具补充细节
        })


        return fusion_result