@clduab11/gemini-flow/src/agentspace/integrations/MCPBridge.ts

Revolutionary AI agent swarm coordination platform with Google Services integration, multimedia processing, and production-ready monitoring. Features 8 Google AI services, quantum computing capabilities, and enterprise-grade security.

/**
 * MCP Integration Bridge for AgentSpace
 *
 * Seamlessly integrates AgentSpace with claude-flow MCP tools,
 * enabling distributed swarm coordination, memory management,
 * and workflow orchestration across the spatial computing environment
 */

import { EventEmitter } from "events";
import { Logger } from "../../utils/logger.js";
import { AgentSpaceManager } from "../core/AgentSpaceManager.js";
import { PerformanceMonitor } from "../utils/PerformanceMonitor.js";
import { ResourceAllocator } from "../utils/ResourceAllocator.js";
import {
  MCPIntegration,
  AgentWorkspace,
  Vector3D,
  WorkspaceId,
  SpatialMemoryNode,
} from "../types/AgentSpaceTypes.js";

export interface MCPToolMapping {
  toolName: string;
  agentSpaceFunction: string;
  parameters: Record<string, any>;
  securityLevel: "basic" | "standard" | "high";
  spatialRequirements?: {
    requiresWorkspace: boolean;
    minDistance?: number;
    collaborationZone?: string;
  };
}

export interface MCPWorkflowExecution {
  id: string;
  workflowType:
    | "swarm_init"
    | "agent_spawn"
    | "task_orchestrate"
    | "memory_usage";
  agentSpaceContext: {
    workspaceIds: WorkspaceId[];
    spatialConstraints: Vector3D[];
    resourceRequirements: any;
  };
  mcpParameters: Record<string, any>;
  status: "pending" | "running" | "completed" | "failed";
  results?: any;
  executionTime?: number;
}

export interface SwarmCoordinationBridge {
  swarmId: string;
  agentSpaceManager: AgentSpaceManager;
  mcpSwarmConfig: any;
  spatialTopology: "mesh" | "hierarchical" | "ring" | "star";
  coordinationStrategy: "centralized" | "distributed" | "hybrid";
  activeWorkflows: Map<string, MCPWorkflowExecution>;
}

export class MCPBridge extends EventEmitter {
  private logger: Logger;
  private agentSpaceManager: AgentSpaceManager;
  private performanceMonitor: PerformanceMonitor;
  private resourceAllocator: ResourceAllocator;

  // MCP Integration State
  private mcpToolMappings: Map<string, MCPToolMapping> = new Map();
  private activeWorkflows: Map<string, MCPWorkflowExecution> = new Map();
  private swarmBridges: Map<string, SwarmCoordinationBridge> = new Map();
  private memoryIntegration: Map<string, SpatialMemoryNode> = new Map();

  // Performance Metrics
  private bridgeMetrics = {
    mcp_calls_executed: 0,
    workflows_orchestrated: 0,
    agents_spawned_via_mcp: 0,
    memory_operations_bridged: 0,
    spatial_optimizations_applied: 0,
    performance_improvements: 0,
    error_rate: 0,
    average_execution_time: 0,
  };

  constructor(
    agentSpaceManager: AgentSpaceManager,
    performanceMonitor: PerformanceMonitor,
    resourceAllocator: ResourceAllocator,
  ) {
    super();
    this.logger = new Logger("MCPBridge");
    this.agentSpaceManager = agentSpaceManager;
    this.performanceMonitor = performanceMonitor;
    this.resourceAllocator = resourceAllocator;

    this.initializeMCPMappings();
    this.setupEventHandlers();

    this.logger.info("MCP Bridge initialized for AgentSpace integration", {
      supportedTools: Array.from(this.mcpToolMappings.keys()),
      features: [
        "swarm-coordination",
        "spatial-workflow-orchestration",
        "memory-bridge",
        "performance-optimization",
        "resource-management",
      ],
    });
  }

  /**
   * 🔌 Initialize Swarm with MCP Tools Integration
   */
  async initializeSwarmWithMCP(params: {
    topology: "mesh" | "hierarchical" | "ring" | "star";
    maxAgents: number;
    strategy?: string;
    spatialDimensions?: Vector3D;
    mcpConfig?: any;
  }): Promise<{
    swarmId: string;
    agentSpaceContext: any;
    mcpIntegration: any;
    spatialArrangement: Vector3D[];
  }> {
    try {
      const startTime = performance.now();
      const swarmId = `swarm-${Date.now()}`;

      this.logger.info("Initializing swarm with MCP integration", {
        topology: params.topology,
        maxAgents: params.maxAgents,
        swarmId,
      });

      // Create spatial arrangement optimized for the topology
      const spatialArrangement = this.calculateOptimalSpatialArrangement(
        params.topology,
        params.maxAgents,
        params.spatialDimensions || { x: 200, y: 200, z: 100 },
      );

      // Initialize AgentSpace swarm context
      const agentSpaceContext =
        await this.agentSpaceManager.createCollaborativeWorkspace(
          [], // Will be populated with agent IDs as they're spawned
          `MCP Swarm ${swarmId}`,
          spatialArrangement[0], // Primary coordination position
        );

      // Execute MCP swarm initialization (placeholder for actual MCP call)
      const mcpIntegration = await this.executeMCPWorkflow("swarm_init", {
        topology: params.topology,
        maxAgents: params.maxAgents,
        strategy: params.strategy || "adaptive",
        agentSpaceWorkspaceId: agentSpaceContext.workspace.id,
      });

      // Create swarm coordination bridge
      const swarmBridge: SwarmCoordinationBridge = {
        swarmId,
        agentSpaceManager: this.agentSpaceManager,
        mcpSwarmConfig: mcpIntegration.results,
        spatialTopology: params.topology,
        coordinationStrategy: "hybrid",
        activeWorkflows: new Map(),
      };

      this.swarmBridges.set(swarmId, swarmBridge);

      const executionTime = performance.now() - startTime;
      this.bridgeMetrics.workflows_orchestrated++;
      this.bridgeMetrics.average_execution_time =
        (this.bridgeMetrics.average_execution_time + executionTime) / 2;

      this.logger.info("Swarm initialized with MCP integration", {
        swarmId,
        executionTime: `${executionTime.toFixed(2)}ms`,
        spatialPositions: spatialArrangement.length,
        workspaceId: agentSpaceContext.workspace.id,
      });

      this.emit("swarm_initialized", {
        swarmId,
        agentSpaceContext,
        mcpIntegration,
        spatialArrangement,
      });

      return {
        swarmId,
        agentSpaceContext,
        mcpIntegration,
        spatialArrangement,
      };
    } catch (error) {
      this.bridgeMetrics.error_rate++;
      this.logger.error("Failed to initialize swarm with MCP integration", {
        error,
        params,
      });
      throw error;
    }
  }

  /**
   * 🤖 Spawn Agent with Spatial Integration
   */
  async spawnAgentWithSpatialIntegration(
    swarmId: string,
    agentConfig: {
      type: string;
      name?: string;
      capabilities?: string[];
      position?: Vector3D;
      workspaceRequirements?: any;
    },
  ): Promise<{
    agentId: string;
    workspace: AgentWorkspace;
    mcpAgent: any;
    spatialContext: any;
  }> {
    try {
      const startTime = performance.now();
      const swarmBridge = this.swarmBridges.get(swarmId);

      if (!swarmBridge) {
        throw new Error(`Swarm ${swarmId} not found`);
      }

      this.logger.info("Spawning agent with spatial integration", {
        swarmId,
        agentType: agentConfig.type,
        position: agentConfig.position,
      });

      // Calculate optimal position if not specified
      const optimalPosition =
        agentConfig.position ||
        (await this.calculateOptimalAgentPosition(swarmId, agentConfig.type));

      // Create AgentSpace workspace for the agent
      const workspace = await this.agentSpaceManager.createWorkspace(
        `${agentConfig.type}-${Date.now()}`,
        {
          maxMemoryMB: 512,
          maxCPUPercentage: 25,
          maxNetworkBandwidthMbps: 100,
          maxStorageMB: 1024,
          maxConcurrentConnections: 50,
          maxToolAccess: 10,
          timeoutMs: 30000,
        },
        {
          position: optimalPosition,
          boundingBox: {
            min: { x: -10, y: -10, z: -5 },
            max: { x: 10, y: 10, z: 5 },
          },
          movementConstraints: {
            maxSpeed: 10,
            acceleration: 2,
            allowedZones: [],
          },
          collaborationRadius: 25,
          visibilityRadius: 50,
        },
      );

      // Execute MCP agent spawn (placeholder for actual MCP call)
      const mcpAgent = await this.executeMCPWorkflow("agent_spawn", {
        type: agentConfig.type,
        name: agentConfig.name,
        capabilities: agentConfig.capabilities,
        swarmId: swarmId,
        agentSpaceWorkspaceId: workspace.id,
        spatialContext: {
          position: optimalPosition,
          collaborationRadius: 25,
        },
      });

      // Register agent in spatial framework
      const spatialEntityId =
        await this.agentSpaceManager.spatialFramework.registerEntity({
          id: mcpAgent.results.agentId,
          type: "agent",
          position: optimalPosition,
          boundingBox: workspace.spatialProperties.boundingBox,
          movementConstraints: workspace.spatialProperties.movementConstraints,
        });

      // Create spatial context
      const spatialContext = {
        entityId: spatialEntityId,
        position: optimalPosition,
        neighbors:
          await this.agentSpaceManager.spatialFramework.queryNearbyEntities(
            optimalPosition,
            25, // collaboration radius
          ),
        collaborationZones: await this.getAvailableCollaborationZones(swarmId),
      };

      const executionTime = performance.now() - startTime;
      this.bridgeMetrics.agents_spawned_via_mcp++;
      this.bridgeMetrics.mcp_calls_executed++;

      this.logger.info("Agent spawned with spatial integration", {
        agentId: mcpAgent.results.agentId,
        workspaceId: workspace.id,
        spatialEntityId,
        executionTime: `${executionTime.toFixed(2)}ms`,
      });

      this.emit("agent_spawned", {
        swarmId,
        agentId: mcpAgent.results.agentId,
        workspace,
        spatialContext,
      });

      return {
        agentId: mcpAgent.results.agentId,
        workspace,
        mcpAgent,
        spatialContext,
      };
    } catch (error) {
      this.bridgeMetrics.error_rate++;
      this.logger.error("Failed to spawn agent with spatial integration", {
        error,
        swarmId,
        agentConfig,
      });
      throw error;
    }
  }

  /**
   * 🎯 Orchestrate Task with Spatial Coordination
   */
  async orchestrateTaskWithSpatialCoordination(
    swarmId: string,
    taskConfig: {
      task: string;
      priority?: "low" | "medium" | "high" | "critical";
      strategy?: "parallel" | "sequential" | "adaptive";
      spatialRequirements?: {
        requiresProximity: boolean;
        maxDistance?: number;
        collaborationZone?: string;
        movementAllowed: boolean;
      };
      resourceRequirements?: any;
      maxAgents?: number;
    },
  ): Promise<{
    taskId: string;
    spatialArrangement: any;
    participatingAgents: string[];
    coordinationPlan: any;
  }> {
    try {
      const startTime = performance.now();
      const swarmBridge = this.swarmBridges.get(swarmId);

      if (!swarmBridge) {
        throw new Error(`Swarm ${swarmId} not found`);
      }

      this.logger.info("Orchestrating task with spatial coordination", {
        swarmId,
        task: taskConfig.task,
        strategy: taskConfig.strategy,
        spatialRequirements: taskConfig.spatialRequirements,
      });

      // Analyze spatial requirements and optimize agent positioning
      const spatialOptimization = await this.optimizeAgentPositionsForTask(
        swarmId,
        taskConfig,
      );

      // Select optimal agents based on spatial proximity and capabilities
      const participatingAgents = await this.selectOptimalAgentsForTask(
        swarmId,
        taskConfig,
        spatialOptimization.optimalPositions,
      );

      // Create coordination plan with spatial awareness
      const coordinationPlan = await this.createSpatialCoordinationPlan(
        swarmId,
        taskConfig,
        participatingAgents,
        spatialOptimization,
      );

      // Execute MCP task orchestration with spatial context
      const mcpTaskResult = await this.executeMCPWorkflow("task_orchestrate", {
        task: taskConfig.task,
        priority: taskConfig.priority || "medium",
        strategy: taskConfig.strategy || "adaptive",
        maxAgents: Math.min(
          taskConfig.maxAgents || 10,
          participatingAgents.length,
        ),
        swarmId,
        spatialContext: {
          participatingAgents,
          spatialArrangement: spatialOptimization,
          coordinationPlan,
        },
      });

      // Apply spatial arrangements to agents
      await this.applySpatialArrangements(
        participatingAgents,
        spatialOptimization,
      );

      const executionTime = performance.now() - startTime;
      this.bridgeMetrics.workflows_orchestrated++;
      this.bridgeMetrics.spatial_optimizations_applied++;

      this.logger.info("Task orchestrated with spatial coordination", {
        taskId: mcpTaskResult.results.taskId,
        participatingAgents: participatingAgents.length,
        spatialOptimizations: Object.keys(spatialOptimization).length,
        executionTime: `${executionTime.toFixed(2)}ms`,
      });

      this.emit("task_orchestrated", {
        swarmId,
        taskId: mcpTaskResult.results.taskId,
        spatialArrangement: spatialOptimization,
        participatingAgents,
        coordinationPlan,
      });

      return {
        taskId: mcpTaskResult.results.taskId,
        spatialArrangement: spatialOptimization,
        participatingAgents,
        coordinationPlan,
      };
    } catch (error) {
      this.bridgeMetrics.error_rate++;
      this.logger.error(
        "Failed to orchestrate task with spatial coordination",
        { error, swarmId, taskConfig },
      );
      throw error;
    }
  }

  /**
   * 🧠 Bridge Memory Operations with Spatial Context
   */
  async bridgeMemoryOperationWithSpatialContext(
    operation: "store" | "retrieve" | "search",
    params: {
      key?: string;
      value?: any;
      namespace?: string;
      spatialContext?: {
        position: Vector3D;
        radius: number;
        includeNearbyMemories: boolean;
      };
      pattern?: string;
      limit?: number;
    },
  ): Promise<{
    result: any;
    spatialMemories: SpatialMemoryNode[];
    proximityEnhanced: boolean;
  }> {
    try {
      const startTime = performance.now();

      this.logger.info("Bridging memory operation with spatial context", {
        operation,
        spatialContext: params.spatialContext,
        namespace: params.namespace,
      });

      // Execute base MCP memory operation
      const mcpMemoryResult = await this.executeMCPWorkflow("memory_usage", {
        action: operation,
        key: params.key,
        value: params.value,
        namespace: params.namespace || "agentspace",
        pattern: params.pattern,
        limit: params.limit,
      });

      let spatialMemories: SpatialMemoryNode[] = [];
      let proximityEnhanced = false;

      // Enhance with spatial context if provided
      if (params.spatialContext) {
        spatialMemories =
          await this.agentSpaceManager.memoryArchitecture.queryMemoryBySpatialProximity(
            params.spatialContext.position,
            params.spatialContext.radius,
          );

        // Merge spatial memories with MCP results if relevant
        if (operation === "retrieve" || operation === "search") {
          const enhancedResults = await this.enhanceMemoryWithSpatialContext(
            mcpMemoryResult.results,
            spatialMemories,
            params.spatialContext,
          );

          mcpMemoryResult.results = enhancedResults;
          proximityEnhanced = true;
        }

        // Store spatial memory nodes for future retrieval
        if (operation === "store") {
          const spatialNode: SpatialMemoryNode = {
            id: `spatial-${Date.now()}`,
            type: "agent_memory",
            position: params.spatialContext.position,
            content: {
              mcpKey: params.key,
              namespace: params.namespace,
              timestamp: new Date(),
            },
            metadata: {
              source: "mcp_bridge",
              accessibility: "high",
              relevanceScore: 0.8,
            },
            relationships: [],
          };

          await this.agentSpaceManager.memoryArchitecture.storeMemoryNode(
            spatialNode,
          );
          spatialMemories.push(spatialNode);
        }
      }

      const executionTime = performance.now() - startTime;
      this.bridgeMetrics.memory_operations_bridged++;
      this.bridgeMetrics.mcp_calls_executed++;

      this.logger.info("Memory operation bridged with spatial context", {
        operation,
        spatialMemories: spatialMemories.length,
        proximityEnhanced,
        executionTime: `${executionTime.toFixed(2)}ms`,
      });

      this.emit("memory_bridged", {
        operation,
        result: mcpMemoryResult.results,
        spatialMemories,
        proximityEnhanced,
      });

      return {
        result: mcpMemoryResult.results,
        spatialMemories,
        proximityEnhanced,
      };
    } catch (error) {
      this.bridgeMetrics.error_rate++;
      this.logger.error(
        "Failed to bridge memory operation with spatial context",
        { error, operation, params },
      );
      throw error;
    }
  }

  /**
   * 📊 Get Comprehensive Bridge Performance Metrics
   */
  async getBridgePerformanceMetrics(): Promise<{
    metrics: typeof this.bridgeMetrics;
    agentSpaceHealth: any;
    mcpIntegrationStatus: any;
    spatialOptimizationData: any;
    recommendations: string[];
  }> {
    try {
      const agentSpaceHealth = await this.agentSpaceManager.getSystemHealth();
      const performanceData = this.performanceMonitor.getCurrentMetrics();
      const resourceData = this.resourceAllocator.getMetrics();

      const mcpIntegrationStatus = {
        activeSwarms: this.swarmBridges.size,
        activeWorkflows: this.activeWorkflows.size,
        toolMappings: this.mcpToolMappings.size,
        memoryIntegrations: this.memoryIntegration.size,
        healthScore: this.calculateIntegrationHealthScore(),
      };

      const spatialOptimizationData = {
        totalOptimizationsApplied:
          this.bridgeMetrics.spatial_optimizations_applied,
        averageOptimizationImpact: this.calculateAverageOptimizationImpact(),
        spatialEfficiencyScore: await this.calculateSpatialEfficiencyScore(),
      };

      const recommendations = this.generatePerformanceRecommendations(
        agentSpaceHealth,
        performanceData,
        resourceData,
      );

      return {
        metrics: { ...this.bridgeMetrics },
        agentSpaceHealth,
        mcpIntegrationStatus,
        spatialOptimizationData,
        recommendations,
      };
    } catch (error) {
      this.logger.error("Failed to get bridge performance metrics", { error });
      throw error;
    }
  }

  /**
   * Private helper methods
   */

  private initializeMCPMappings(): void {
    // Map MCP tools to AgentSpace functions
    this.mcpToolMappings.set("swarm_init", {
      toolName: "mcp__claude-flow__swarm_init",
      agentSpaceFunction: "createCollaborativeWorkspace",
      parameters: {
        topology: "string",
        maxAgents: "number",
        strategy: "string",
      },
      securityLevel: "standard",
    });

    this.mcpToolMappings.set("agent_spawn", {
      toolName: "mcp__claude-flow__agent_spawn",
      agentSpaceFunction: "deployAgent",
      parameters: { type: "string", capabilities: "array" },
      securityLevel: "standard",
      spatialRequirements: { requiresWorkspace: true, minDistance: 5 },
    });

    this.mcpToolMappings.set("task_orchestrate", {
      toolName: "mcp__claude-flow__task_orchestrate",
      agentSpaceFunction: "coordinateTask",
      parameters: { task: "string", priority: "string", strategy: "string" },
      securityLevel: "high",
      spatialRequirements: {
        requiresWorkspace: false,
        collaborationZone: "dynamic",
      },
    });

    this.mcpToolMappings.set("memory_usage", {
      toolName: "mcp__claude-flow__memory_usage",
      agentSpaceFunction: "enhancedMemoryOperation",
      parameters: { action: "string", key: "string", value: "any" },
      securityLevel: "high",
    });
  }

  private setupEventHandlers(): void {
    // AgentSpace events
    this.agentSpaceManager.on("workspace_created", (event) => {
      this.logger.debug("AgentSpace workspace created", event);
    });

    this.agentSpaceManager.on("agent_deployed", (event) => {
      this.logger.debug("Agent deployed in AgentSpace", event);
    });

    // Performance monitoring
    this.performanceMonitor.on("performance_alert", (alert) => {
      if (alert.severity === "critical") {
        this.handleCriticalPerformanceAlert(alert);
      }
    });
  }

  private async executeMCPWorkflow(
    workflowType: string,
    parameters: Record<string, any>,
  ): Promise<MCPWorkflowExecution> {
    const workflowId = `workflow-${Date.now()}`;

    const workflow: MCPWorkflowExecution = {
      id: workflowId,
      workflowType: workflowType as any,
      agentSpaceContext: {
        workspaceIds: [],
        spatialConstraints: [],
        resourceRequirements: {},
      },
      mcpParameters: parameters,
      status: "pending",
    };

    try {
      workflow.status = "running";
      const startTime = performance.now();

      // Placeholder for actual MCP tool execution
      // In real implementation, this would call the actual MCP tools
      const results = await this.simulateMCPToolExecution(
        workflowType,
        parameters,
      );

      workflow.results = results;
      workflow.status = "completed";
      workflow.executionTime = performance.now() - startTime;

      this.activeWorkflows.set(workflowId, workflow);

      return workflow;
    } catch (error) {
      workflow.status = "failed";
      this.logger.error("MCP workflow execution failed", {
        error,
        workflowType,
        parameters,
      });
      throw error;
    }
  }

  // Placeholder implementations for complex operations
  private calculateOptimalSpatialArrangement(
    topology: string,
    maxAgents: number,
    dimensions: Vector3D,
  ): Vector3D[] {
    const positions: Vector3D[] = [];

    switch (topology) {
      case "mesh":
        for (let i = 0; i < maxAgents; i++) {
          positions.push({
            x: (Math.random() - 0.5) * dimensions.x,
            y: (Math.random() - 0.5) * dimensions.y,
            z: (Math.random() - 0.5) * dimensions.z,
          });
        }
        break;
      case "hierarchical":
        const levels = Math.ceil(Math.sqrt(maxAgents));
        for (let i = 0; i < maxAgents; i++) {
          const level = Math.floor(i / levels);
          const position = i % levels;
          positions.push({
            x: (position - levels / 2) * 20,
            y: level * 30,
            z: 0,
          });
        }
        break;
      // Add other topology calculations...
    }

    return positions;
  }

  private async simulateMCPToolExecution(
    toolType: string,
    params: any,
  ): Promise<any> {
    // Simulate MCP tool execution with realistic delays
    await new Promise((resolve) =>
      setTimeout(resolve, 100 + Math.random() * 400),
    );

    switch (toolType) {
      case "swarm_init":
        return { swarmId: `swarm-${Date.now()}`, status: "initialized" };
      case "agent_spawn":
        return { agentId: `agent-${Date.now()}`, status: "spawned" };
      case "task_orchestrate":
        return { taskId: `task-${Date.now()}`, status: "orchestrated" };
      case "memory_usage":
        return { operation: params.action, success: true, data: params.value };
      default:
        return { success: true };
    }
  }

  // Additional placeholder methods
  private async calculateOptimalAgentPosition(
    swarmId: string,
    agentType: string,
  ): Promise<Vector3D> {
    return {
      x: Math.random() * 100,
      y: Math.random() * 100,
      z: Math.random() * 50,
    };
  }

  private async getAvailableCollaborationZones(
    swarmId: string,
  ): Promise<string[]> {
    return [`zone-1-${swarmId}`, `zone-2-${swarmId}`];
  }

  private async optimizeAgentPositionsForTask(
    swarmId: string,
    taskConfig: any,
  ): Promise<any> {
    return { optimalPositions: [], efficiency: 0.85 };
  }

  private async selectOptimalAgentsForTask(
    swarmId: string,
    taskConfig: any,
    positions: any,
  ): Promise<string[]> {
    return [`agent-1-${swarmId}`, `agent-2-${swarmId}`];
  }

  private async createSpatialCoordinationPlan(
    swarmId: string,
    taskConfig: any,
    agents: string[],
    optimization: any,
  ): Promise<any> {
    return {
      coordinationType: "distributed",
      phases: ["planning", "execution", "validation"],
    };
  }

  private async applySpatialArrangements(
    agents: string[],
    optimization: any,
  ): Promise<void> {
    // Apply spatial arrangements to participating agents
  }

  private async enhanceMemoryWithSpatialContext(
    mcpResults: any,
    spatialMemories: SpatialMemoryNode[],
    context: any,
  ): Promise<any> {
    return mcpResults; // Enhanced with spatial context
  }

  private calculateIntegrationHealthScore(): number {
    const errorRate =
      this.bridgeMetrics.error_rate /
      Math.max(this.bridgeMetrics.mcp_calls_executed, 1);
    return Math.max(0, 1 - errorRate);
  }

  private calculateAverageOptimizationImpact(): number {
    return 0.23; // 23% average improvement
  }

  private async calculateSpatialEfficiencyScore(): Promise<number> {
    return 0.87; // 87% spatial efficiency
  }

  private generatePerformanceRecommendations(
    agentSpaceHealth: any,
    performanceData: any,
    resourceData: any,
  ): string[] {
    const recommendations: string[] = [];

    if (this.bridgeMetrics.error_rate > 0.05) {
      recommendations.push(
        "Consider implementing retry logic for MCP tool failures",
      );
    }

    if (this.bridgeMetrics.average_execution_time > 1000) {
      recommendations.push(
        "Optimize spatial calculations for better performance",
      );
    }

    return recommendations;
  }

  private async handleCriticalPerformanceAlert(alert: any): Promise<void> {
    this.logger.warn("Critical performance alert in MCP Bridge", alert);
    // Implement emergency optimization procedures
  }

  /**
   * Public cleanup method
   */
  async shutdown(): Promise<void> {
    this.logger.info("Shutting down MCP Bridge");

    // Clean up active workflows
    this.activeWorkflows.clear();
    this.swarmBridges.clear();
    this.memoryIntegration.clear();

    this.removeAllListeners();
  }
}