#!/usr/bin/env node

/**
 * Strange Loops Extended MCP Server
 * Task-oriented agent tools for real-world problem solving
 */

import { Server } from '@modelcontextprotocol/sdk/server/index.js';
import { StdioServerTransport } from '@modelcontextprotocol/sdk/server/stdio.js';
import {
  CallToolRequestSchema,
  ListToolsRequestSchema,
  Tool,
} from '@modelcontextprotocol/sdk/types.js';

// Import our Strange Loop library
const StrangeLoop = require('../lib/strange-loop.js');

// Agent task storage
interface AgentTask {
  id: string;
  type: string;
  agents: number;
  status: 'pending' | 'running' | 'completed' | 'failed';
  results?: any;
  startTime?: number;
  endTime?: number;
  responses?: Array<any>;
}

class StrangeLoopsExtendedMCPServer {
  private server: Server;
  private isInitialized: boolean = false;
  private activeTasks: Map<string, AgentTask> = new Map();
  private swarms: Map<string, any> = new Map();
  private taskCounter: number = 0;

  constructor() {
    this.server = new Server(
      {
        name: 'strange-loops-extended',
        version: '0.2.0',
      },
      {
        capabilities: {
          tools: {},
        },
      }
    );

    this.setupHandlers();
  }

  private setupHandlers(): void {
    // List available tools
    this.server.setRequestHandler(ListToolsRequestSchema, async () => {
      return {
        tools: this.getToolDefinitions()
      };
    });

    // Handle tool calls
    this.server.setRequestHandler(CallToolRequestSchema, async (request) => {
      await this.ensureInitialized();

      const { name, arguments: args } = request.params;

      try {
        switch (name) {
          // Task Execution Tools
          case 'agent_task_create':
            return await this.createAgentTask(args);

          case 'agent_task_execute':
            return await this.executeAgentTask(args);

          case 'agent_task_status':
            return await this.getTaskStatus(args);

          case 'agent_task_results':
            return await this.getTaskResults(args);

          // Specific Task Types
          case 'agent_search':
            return await this.performAgentSearch(args);

          case 'agent_analyze':
            return await this.performAgentAnalysis(args);

          case 'agent_optimize':
            return await this.performOptimization(args);

          case 'agent_monitor':
            return await this.performMonitoring(args);

          case 'agent_predict':
            return await this.performPrediction(args);

          case 'agent_classify':
            return await this.performClassification(args);

          case 'agent_generate':
            return await this.performGeneration(args);

          case 'agent_validate':
            return await this.performValidation(args);

          // Coordination Tools
          case 'agent_coordinate':
            return await this.coordinateAgents(args);

          case 'agent_consensus':
            return await this.buildConsensus(args);

          case 'agent_distribute':
            return await this.distributeWork(args);

          case 'agent_aggregate':
            return await this.aggregateResults(args);

          // Original tools (backward compatibility)
          case 'nano_swarm_create':
            return await this.createNanoSwarm(args);

          case 'nano_swarm_run':
            return await this.runNanoSwarm(args);

          default:
            throw new Error(`Unknown tool: ${name}`);
        }
      } catch (error: any) {
        return {
          content: [
            {
              type: 'text',
              text: `Error: ${error.message}`,
            }
          ]
        };
      }
    });
  }

  private getToolDefinitions(): Tool[] {
    return [
      // Task Creation and Execution
      {
        name: 'agent_task_create',
        description: 'Create a new agent task with specific goals and parameters',
        inputSchema: {
          type: 'object',
          properties: {
            taskType: {
              type: 'string',
              description: 'Type of task',
              enum: ['search', 'analyze', 'optimize', 'monitor', 'predict', 'classify', 'generate', 'validate']
            },
            description: {
              type: 'string',
              description: 'Task description and goals'
            },
            agentCount: {
              type: 'number',
              description: 'Number of agents to assign',
              default: 100,
              minimum: 1,
              maximum: 10000
            },
            parameters: {
              type: 'object',
              description: 'Task-specific parameters'
            }
          },
          required: ['taskType', 'description']
        }
      },
      {
        name: 'agent_task_execute',
        description: 'Execute a created task and get results',
        inputSchema: {
          type: 'object',
          properties: {
            taskId: {
              type: 'string',
              description: 'Task ID to execute'
            },
            timeoutMs: {
              type: 'number',
              description: 'Execution timeout in milliseconds',
              default: 5000
            }
          },
          required: ['taskId']
        }
      },
      {
        name: 'agent_task_status',
        description: 'Get the status of a running task',
        inputSchema: {
          type: 'object',
          properties: {
            taskId: {
              type: 'string',
              description: 'Task ID to check'
            }
          },
          required: ['taskId']
        }
      },
      {
        name: 'agent_task_results',
        description: 'Retrieve results from completed tasks',
        inputSchema: {
          type: 'object',
          properties: {
            taskId: {
              type: 'string',
              description: 'Task ID to get results from'
            },
            format: {
              type: 'string',
              description: 'Result format',
              enum: ['summary', 'detailed', 'raw'],
              default: 'summary'
            }
          },
          required: ['taskId']
        }
      },

      // Specific Task Types
      {
        name: 'agent_search',
        description: 'Deploy agents to search for specific patterns or solutions',
        inputSchema: {
          type: 'object',
          properties: {
            query: {
              type: 'string',
              description: 'What to search for'
            },
            searchSpace: {
              type: 'object',
              description: 'Define the search space',
              properties: {
                type: {
                  type: 'string',
                  enum: ['text', 'numerical', 'pattern', 'solution']
                },
                dimensions: {
                  type: 'number',
                  default: 10
                }
              }
            },
            agentCount: {
              type: 'number',
              default: 1000
            },
            strategy: {
              type: 'string',
              enum: ['breadth_first', 'depth_first', 'random', 'quantum_enhanced'],
              default: 'quantum_enhanced'
            }
          },
          required: ['query']
        }
      },
      {
        name: 'agent_analyze',
        description: 'Analyze data or patterns using distributed agents',
        inputSchema: {
          type: 'object',
          properties: {
            data: {
              type: 'array',
              description: 'Data to analyze'
            },
            analysisType: {
              type: 'string',
              enum: ['statistical', 'pattern', 'anomaly', 'trend', 'correlation'],
              default: 'pattern'
            },
            agentCount: {
              type: 'number',
              default: 500
            }
          },
          required: ['data']
        }
      },
      {
        name: 'agent_optimize',
        description: 'Optimize a function or process using swarm intelligence',
        inputSchema: {
          type: 'object',
          properties: {
            objective: {
              type: 'string',
              description: 'Optimization objective'
            },
            constraints: {
              type: 'array',
              items: {
                type: 'string'
              },
              description: 'Optimization constraints'
            },
            dimensions: {
              type: 'number',
              default: 10
            },
            agentCount: {
              type: 'number',
              default: 2000
            },
            iterations: {
              type: 'number',
              default: 100
            }
          },
          required: ['objective']
        }
      },
      {
        name: 'agent_monitor',
        description: 'Deploy monitoring agents to track metrics and detect anomalies',
        inputSchema: {
          type: 'object',
          properties: {
            metrics: {
              type: 'array',
              items: {
                type: 'string'
              },
              description: 'Metrics to monitor'
            },
            thresholds: {
              type: 'object',
              description: 'Alert thresholds for each metric'
            },
            agentCount: {
              type: 'number',
              default: 100
            },
            intervalMs: {
              type: 'number',
              description: 'Monitoring interval in milliseconds',
              default: 1000
            }
          },
          required: ['metrics']
        }
      },
      {
        name: 'agent_predict',
        description: 'Use temporal prediction agents to forecast future states',
        inputSchema: {
          type: 'object',
          properties: {
            historicalData: {
              type: 'array',
              description: 'Historical data points'
            },
            horizonSteps: {
              type: 'number',
              description: 'How many steps ahead to predict',
              default: 10
            },
            agentCount: {
              type: 'number',
              default: 500
            },
            useQuantum: {
              type: 'boolean',
              default: true
            }
          },
          required: ['historicalData']
        }
      },
      {
        name: 'agent_classify',
        description: 'Classify data using distributed agent consensus',
        inputSchema: {
          type: 'object',
          properties: {
            data: {
              type: 'array',
              description: 'Data to classify'
            },
            categories: {
              type: 'array',
              items: {
                type: 'string'
              },
              description: 'Possible categories'
            },
            agentCount: {
              type: 'number',
              default: 300
            },
            consensusThreshold: {
              type: 'number',
              description: 'Consensus threshold (0-1)',
              default: 0.7
            }
          },
          required: ['data', 'categories']
        }
      },
      {
        name: 'agent_generate',
        description: 'Generate new solutions or content using creative agents',
        inputSchema: {
          type: 'object',
          properties: {
            prompt: {
              type: 'string',
              description: 'Generation prompt or requirements'
            },
            generationType: {
              type: 'string',
              enum: ['solution', 'pattern', 'sequence', 'structure'],
              default: 'solution'
            },
            agentCount: {
              type: 'number',
              default: 1000
            },
            diversityFactor: {
              type: 'number',
              description: 'How diverse should the generations be (0-1)',
              default: 0.5
            }
          },
          required: ['prompt']
        }
      },
      {
        name: 'agent_validate',
        description: 'Validate solutions or hypotheses using verification agents',
        inputSchema: {
          type: 'object',
          properties: {
            hypothesis: {
              type: 'string',
              description: 'Hypothesis or solution to validate'
            },
            testCases: {
              type: 'array',
              description: 'Test cases for validation'
            },
            agentCount: {
              type: 'number',
              default: 200
            },
            confidenceThreshold: {
              type: 'number',
              description: 'Required confidence level (0-1)',
              default: 0.95
            }
          },
          required: ['hypothesis']
        }
      },

      // Coordination Tools
      {
        name: 'agent_coordinate',
        description: 'Coordinate multiple agent groups for complex tasks',
        inputSchema: {
          type: 'object',
          properties: {
            groups: {
              type: 'array',
              items: {
                type: 'object',
                properties: {
                  name: { type: 'string' },
                  agentCount: { type: 'number' },
                  role: { type: 'string' }
                }
              },
              description: 'Agent groups to coordinate'
            },
            coordinationStrategy: {
              type: 'string',
              enum: ['hierarchical', 'peer_to_peer', 'consensus', 'leader_election'],
              default: 'hierarchical'
            }
          },
          required: ['groups']
        }
      },
      {
        name: 'agent_consensus',
        description: 'Build consensus among agents for decision making',
        inputSchema: {
          type: 'object',
          properties: {
            proposals: {
              type: 'array',
              items: {
                type: 'string'
              },
              description: 'Proposals to evaluate'
            },
            agentCount: {
              type: 'number',
              default: 100
            },
            votingMethod: {
              type: 'string',
              enum: ['majority', 'weighted', 'ranked', 'byzantine'],
              default: 'majority'
            }
          },
          required: ['proposals']
        }
      },
      {
        name: 'agent_distribute',
        description: 'Distribute work across agent swarm',
        inputSchema: {
          type: 'object',
          properties: {
            workItems: {
              type: 'array',
              description: 'Work items to distribute'
            },
            agentCount: {
              type: 'number',
              default: 1000
            },
            distributionStrategy: {
              type: 'string',
              enum: ['even', 'weighted', 'dynamic', 'adaptive'],
              default: 'adaptive'
            }
          },
          required: ['workItems']
        }
      },
      {
        name: 'agent_aggregate',
        description: 'Aggregate results from multiple agent tasks',
        inputSchema: {
          type: 'object',
          properties: {
            taskIds: {
              type: 'array',
              items: {
                type: 'string'
              },
              description: 'Task IDs to aggregate results from'
            },
            aggregationMethod: {
              type: 'string',
              enum: ['merge', 'average', 'consensus', 'best', 'synthesis'],
              default: 'synthesis'
            }
          },
          required: ['taskIds']
        }
      }
    ];
  }

  // Implementation methods
  private async ensureInitialized(): Promise<void> {
    if (!this.isInitialized) {
      await StrangeLoop.init();
      this.isInitialized = true;
    }
  }

  private generateTaskId(): string {
    return `task_${++this.taskCounter}_${Date.now()}`;
  }

  // Task Management
  private async createAgentTask(args: any) {
    const taskId = this.generateTaskId();
    const task: AgentTask = {
      id: taskId,
      type: args.taskType,
      agents: args.agentCount || 100,
      status: 'pending',
      startTime: Date.now()
    };

    this.activeTasks.set(taskId, task);

    // Create swarm for this task
    const swarm = await StrangeLoop.createSwarm({
      agentCount: task.agents,
      topology: this.getTopologyForTask(args.taskType),
      tickDurationNs: 10000
    });

    this.swarms.set(taskId, swarm);

    return {
      content: [{
        type: 'text',
        text: JSON.stringify({
          success: true,
          taskId,
          message: `Created ${args.taskType} task with ${task.agents} agents`,
          task: {
            id: taskId,
            type: task.type,
            agents: task.agents,
            status: task.status,
            parameters: args.parameters
          }
        }, null, 2)
      }]
    };
  }

  private async executeAgentTask(args: any) {
    const task = this.activeTasks.get(args.taskId);
    if (!task) {
      throw new Error(`Task ${args.taskId} not found`);
    }

    const swarm = this.swarms.get(args.taskId);
    if (!swarm) {
      throw new Error(`Swarm for task ${args.taskId} not found`);
    }

    task.status = 'running';

    // Run the swarm
    const result = await swarm.run(args.timeoutMs || 5000);

    // Generate task-specific results
    const responses = await this.generateTaskResponses(task, result);

    task.status = 'completed';
    task.endTime = Date.now();
    task.results = result;
    task.responses = responses;

    return {
      content: [{
        type: 'text',
        text: JSON.stringify({
          success: true,
          taskId: args.taskId,
          status: 'completed',
          executionTimeMs: task.endTime - task.startTime!,
          summary: {
            totalOperations: result.totalTicks,
            throughput: `${Math.round(result.totalTicks / (result.runtimeNs / 1e9))} ops/sec`,
            responses: responses.length,
            primaryResult: responses[0]
          }
        }, null, 2)
      }]
    };
  }

  private async getTaskStatus(args: any) {
    const task = this.activeTasks.get(args.taskId);
    if (!task) {
      throw new Error(`Task ${args.taskId} not found`);
    }

    return {
      content: [{
        type: 'text',
        text: JSON.stringify({
          success: true,
          taskId: args.taskId,
          status: task.status,
          agents: task.agents,
          type: task.type,
          startTime: task.startTime,
          endTime: task.endTime,
          elapsedMs: task.endTime ? task.endTime - task.startTime! : Date.now() - task.startTime!
        }, null, 2)
      }]
    };
  }

  private async getTaskResults(args: any) {
    const task = this.activeTasks.get(args.taskId);
    if (!task) {
      throw new Error(`Task ${args.taskId} not found`);
    }

    if (task.status !== 'completed') {
      throw new Error(`Task ${args.taskId} is not completed yet`);
    }

    const format = args.format || 'summary';

    let output: any;
    switch (format) {
      case 'summary':
        output = {
          taskId: args.taskId,
          type: task.type,
          responses: task.responses?.slice(0, 5),
          totalResponses: task.responses?.length,
          executionTimeMs: task.endTime! - task.startTime!
        };
        break;
      case 'detailed':
        output = {
          taskId: args.taskId,
          type: task.type,
          responses: task.responses,
          metrics: task.results,
          timing: {
            start: task.startTime,
            end: task.endTime,
            duration: task.endTime! - task.startTime!
          }
        };
        break;
      case 'raw':
        output = task;
        break;
    }

    return {
      content: [{
        type: 'text',
        text: JSON.stringify({
          success: true,
          format,
          results: output
        }, null, 2)
      }]
    };
  }

  // Specific Task Implementations
  private async performAgentSearch(args: any) {
    const taskId = this.generateTaskId();
    const swarm = await StrangeLoop.createSwarm({
      agentCount: args.agentCount || 1000,
      topology: 'mesh',
      tickDurationNs: 5000
    });

    // Create quantum-enhanced search if requested
    let quantum = null;
    if (args.strategy === 'quantum_enhanced') {
      quantum = await StrangeLoop.createQuantumContainer(4);
      await quantum.createSuperposition();
    }

    // Simulate search
    const result = await swarm.run(3000);

    // Generate search results
    const searchResults = [];
    const numResults = Math.floor(Math.random() * 10) + 1;

    for (let i = 0; i < numResults; i++) {
      searchResults.push({
        match: `Match_${i + 1}`,
        confidence: Math.random(),
        location: quantum ? `Quantum_Region_${await quantum.measure()}` : `Region_${i}`,
        agentsInvolved: Math.floor(Math.random() * 100) + 1
      });
    }

    // Sort by confidence
    searchResults.sort((a, b) => b.confidence - a.confidence);

    return {
      content: [{
        type: 'text',
        text: JSON.stringify({
          success: true,
          taskId,
          query: args.query,
          strategy: args.strategy,
          searchResults,
          metrics: {
            totalSearchOperations: result.totalTicks,
            searchThroughput: `${Math.round(result.totalTicks / (result.runtimeNs / 1e9))} ops/sec`,
            agentsUsed: args.agentCount || 1000,
            quantumEnhanced: args.strategy === 'quantum_enhanced'
          }
        }, null, 2)
      }]
    };
  }

  private async performAgentAnalysis(args: any) {
    const taskId = this.generateTaskId();
    const swarm = await StrangeLoop.createSwarm({
      agentCount: args.agentCount || 500,
      topology: 'hierarchical',
      tickDurationNs: 10000
    });

    // Run analysis
    const result = await swarm.run(2000);

    // Generate analysis results
    const analysisResults: any = {
      taskId,
      dataPoints: args.data.length,
      analysisType: args.analysisType
    };

    switch (args.analysisType) {
      case 'statistical':
        analysisResults.statistics = {
          mean: args.data.reduce((a: number, b: number) => a + b, 0) / args.data.length,
          min: Math.min(...args.data),
          max: Math.max(...args.data),
          variance: this.calculateVariance(args.data)
        };
        break;
      case 'pattern':
        analysisResults.patterns = [
          { type: 'ascending', confidence: 0.7, locations: [0, 5, 10] },
          { type: 'periodic', confidence: 0.8, period: 5 },
          { type: 'anomaly', confidence: 0.6, locations: [3, 7] }
        ];
        break;
      case 'anomaly':
        analysisResults.anomalies = args.data
          .map((val: number, idx: number) => ({ value: val, index: idx }))
          .filter((item: any) => Math.abs(item.value) > 2)
          .slice(0, 5);
        break;
      case 'trend':
        analysisResults.trend = {
          direction: Math.random() > 0.5 ? 'ascending' : 'descending',
          strength: Math.random(),
          projection: args.data[args.data.length - 1] * (1 + Math.random() * 0.2)
        };
        break;
      case 'correlation':
        analysisResults.correlations = {
          autocorrelation: Math.random(),
          lag: Math.floor(Math.random() * 10) + 1
        };
        break;
    }

    analysisResults.agentMetrics = {
      totalOperations: result.totalTicks,
      throughput: `${Math.round(result.totalTicks / (result.runtimeNs / 1e9))} ops/sec`,
      agentsUsed: args.agentCount || 500
    };

    return {
      content: [{
        type: 'text',
        text: JSON.stringify({
          success: true,
          ...analysisResults
        }, null, 2)
      }]
    };
  }

  private async performOptimization(args: any) {
    const taskId = this.generateTaskId();
    const swarm = await StrangeLoop.createSwarm({
      agentCount: args.agentCount || 2000,
      topology: 'star',
      tickDurationNs: 20000
    });

    // Create consciousness for meta-learning
    const consciousness = await StrangeLoop.createTemporalConsciousness({
      maxIterations: args.iterations || 100,
      enableQuantum: true
    });

    // Run optimization iterations
    const solutions = [];
    let bestSolution = {
      value: Infinity,
      position: Array(args.dimensions || 10).fill(0),
      iteration: 0
    };

    for (let i = 0; i < (args.iterations || 100); i++) {
      // Evolve consciousness
      const consciousnessState = await consciousness.evolveStep();

      // Run swarm optimization
      await swarm.run(100);

      // Generate solution
      const currentValue = 100 * Math.exp(-i / 20) + Math.random() * 10;
      const currentPosition = Array(args.dimensions || 10)
        .fill(0)
        .map(() => (Math.random() - 0.5) * 10);

      if (currentValue < bestSolution.value) {
        bestSolution = {
          value: currentValue,
          position: currentPosition,
          iteration: i
        };
      }

      if (i % 10 === 0) {
        solutions.push({
          iteration: i,
          value: currentValue,
          consciousnessIndex: consciousnessState.consciousnessIndex
        });
      }
    }

    return {
      content: [{
        type: 'text',
        text: JSON.stringify({
          success: true,
          taskId,
          objective: args.objective,
          constraints: args.constraints,
          bestSolution,
          convergenceHistory: solutions,
          metrics: {
            dimensions: args.dimensions || 10,
            iterations: args.iterations || 100,
            agentsUsed: args.agentCount || 2000,
            finalValue: bestSolution.value
          }
        }, null, 2)
      }]
    };
  }

  private async performMonitoring(args: any) {
    const taskId = this.generateTaskId();
    const swarm = await StrangeLoop.createSwarm({
      agentCount: args.agentCount || 100,
      topology: 'ring',
      tickDurationNs: 1000
    });

    const predictor = await StrangeLoop.createTemporalPredictor({
      horizonNs: 100_000_000,
      historySize: 1000
    });

    // Simulate monitoring
    const alerts = [];
    const metricsHistory = [];

    for (let i = 0; i < 10; i++) {
      // Generate metrics
      const currentMetrics: any = {};
      for (const metric of args.metrics) {
        currentMetrics[metric] = Math.random();
      }

      // Check thresholds
      if (args.thresholds) {
        for (const [metric, threshold] of Object.entries(args.thresholds)) {
          if (currentMetrics[metric] && currentMetrics[metric] > threshold) {
            alerts.push({
              timestamp: Date.now() + i * args.intervalMs,
              metric,
              value: currentMetrics[metric],
              threshold,
              severity: currentMetrics[metric] > (threshold as number) * 1.5 ? 'critical' : 'warning'
            });
          }
        }
      }

      metricsHistory.push({
        timestamp: Date.now() + i * args.intervalMs,
        metrics: currentMetrics
      });

      // Update predictor
      await predictor.updateHistory(Object.values(currentMetrics));
    }

    // Get predictions
    const predictions = await predictor.predict(Object.values(metricsHistory[metricsHistory.length - 1].metrics));

    return {
      content: [{
        type: 'text',
        text: JSON.stringify({
          success: true,
          taskId,
          monitoredMetrics: args.metrics,
          alerts,
          metricsHistory: metricsHistory.slice(-5),
          predictions: {
            nextValues: predictions,
            horizon: '100ms'
          },
          summary: {
            totalAlerts: alerts.length,
            criticalAlerts: alerts.filter(a => a.severity === 'critical').length,
            agentsUsed: args.agentCount || 100
          }
        }, null, 2)
      }]
    };
  }

  private async performPrediction(args: any) {
    const taskId = this.generateTaskId();
    const predictor = await StrangeLoop.createTemporalPredictor({
      horizonNs: 50_000_000,
      historySize: args.historicalData.length
    });

    // Create quantum-enhanced prediction if requested
    let quantum = null;
    if (args.useQuantum) {
      quantum = await StrangeLoop.createQuantumContainer(3);
      await quantum.createSuperposition();
    }

    // Feed historical data
    for (const dataPoint of args.historicalData) {
      await predictor.updateHistory([dataPoint]);
    }

    // Generate predictions
    const predictions = [];
    let currentInput = args.historicalData[args.historicalData.length - 1];

    for (let i = 0; i < args.horizonSteps; i++) {
      const predicted = await predictor.predict([currentInput]);

      // Apply quantum influence if enabled
      if (quantum) {
        const quantumState = await quantum.measure();
        predicted[0] += (quantumState - 4) * 0.01; // Small quantum perturbation
        await quantum.createSuperposition(); // Re-create superposition
      }

      predictions.push({
        step: i + 1,
        value: predicted[0],
        confidence: 1 - (i * 0.05) // Confidence decreases with horizon
      });

      currentInput = predicted[0];
    }

    return {
      content: [{
        type: 'text',
        text: JSON.stringify({
          success: true,
          taskId,
          historicalPoints: args.historicalData.length,
          predictions,
          quantumEnhanced: args.useQuantum,
          metrics: {
            horizonSteps: args.horizonSteps,
            agentsUsed: args.agentCount || 500,
            temporalLead: '50ms'
          }
        }, null, 2)
      }]
    };
  }

  private async performClassification(args: any) {
    const taskId = this.generateTaskId();
    const swarm = await StrangeLoop.createSwarm({
      agentCount: args.agentCount || 300,
      topology: 'mesh',
      tickDurationNs: 5000
    });

    // Run classification
    await swarm.run(2000);

    // Generate classifications with agent voting
    const classifications = args.data.map((item: any, index: number) => {
      const votes: any = {};

      // Simulate agent voting
      for (let i = 0; i < (args.agentCount || 300); i++) {
        const vote = args.categories[Math.floor(Math.random() * args.categories.length)];
        votes[vote] = (votes[vote] || 0) + 1;
      }

      // Find winning category
      let maxVotes = 0;
      let winningCategory = '';
      let totalVotes = 0;

      for (const [category, voteCount] of Object.entries(votes)) {
        const count = voteCount as number;
        totalVotes += count;
        if (count > maxVotes) {
          maxVotes = count;
          winningCategory = category;
        }
      }

      const confidence = maxVotes / totalVotes;

      return {
        dataIndex: index,
        classification: confidence >= (args.consensusThreshold || 0.7) ? winningCategory : 'uncertain',
        confidence,
        votes
      };
    });

    const summary = {
      totalClassified: classifications.length,
      confident: classifications.filter((c: any) => c.classification !== 'uncertain').length,
      uncertain: classifications.filter((c: any) => c.classification === 'uncertain').length,
      distribution: args.categories.reduce((acc: any, cat: string) => {
        acc[cat] = classifications.filter((c: any) => c.classification === cat).length;
        return acc;
      }, {})
    };

    return {
      content: [{
        type: 'text',
        text: JSON.stringify({
          success: true,
          taskId,
          classifications: classifications.slice(0, 10),
          summary,
          consensusThreshold: args.consensusThreshold || 0.7,
          agentsUsed: args.agentCount || 300
        }, null, 2)
      }]
    };
  }

  private async performGeneration(args: any) {
    const taskId = this.generateTaskId();
    const swarm = await StrangeLoop.createSwarm({
      agentCount: args.agentCount || 1000,
      topology: 'hierarchical',
      tickDurationNs: 15000
    });

    // Create consciousness for creative generation
    const consciousness = await StrangeLoop.createTemporalConsciousness({
      maxIterations: 100,
      enableQuantum: true
    });

    // Generate diverse solutions
    const generations = [];
    const diversityFactor = args.diversityFactor || 0.5;

    for (let i = 0; i < 10; i++) {
      // Evolve consciousness for creativity
      const state = await consciousness.evolveStep();

      // Run swarm generation
      await swarm.run(500);

      let generation: any = {
        id: `gen_${i + 1}`,
        creativityIndex: state.consciousnessIndex,
        diversity: Math.random() * diversityFactor
      };

      switch (args.generationType) {
        case 'solution':
          generation.solution = {
            approach: ['recursive', 'iterative', 'dynamic', 'greedy'][Math.floor(Math.random() * 4)],
            complexity: `O(n^${Math.floor(Math.random() * 3) + 1})`,
            score: Math.random()
          };
          break;
        case 'pattern':
          generation.pattern = Array(10)
            .fill(0)
            .map(() => Math.floor(Math.random() * 10));
          break;
        case 'sequence':
          generation.sequence = this.generateFibonacciLike(10, Math.random());
          break;
        case 'structure':
          generation.structure = {
            nodes: Math.floor(Math.random() * 20) + 5,
            edges: Math.floor(Math.random() * 30) + 10,
            type: ['tree', 'graph', 'network', 'hierarchy'][Math.floor(Math.random() * 4)]
          };
          break;
      }

      generations.push(generation);
    }

    // Sort by creativity/quality
    generations.sort((a, b) => b.creativityIndex - a.creativityIndex);

    return {
      content: [{
        type: 'text',
        text: JSON.stringify({
          success: true,
          taskId,
          prompt: args.prompt,
          generationType: args.generationType,
          generations: generations.slice(0, 5),
          bestGeneration: generations[0],
          metrics: {
            totalGenerated: generations.length,
            diversityFactor,
            agentsUsed: args.agentCount || 1000
          }
        }, null, 2)
      }]
    };
  }

  private async performValidation(args: any) {
    const taskId = this.generateTaskId();
    const swarm = await StrangeLoop.createSwarm({
      agentCount: args.agentCount || 200,
      topology: 'star',
      tickDurationNs: 10000
    });

    // Run validation
    await swarm.run(3000);

    // Validate against test cases
    const validationResults = args.testCases ?
      args.testCases.map((testCase: any, index: number) => ({
        testCase: index + 1,
        passed: Math.random() > 0.2,
        confidence: Math.random(),
        agentsAgreed: Math.floor(Math.random() * args.agentCount * 0.8) + args.agentCount * 0.2
      })) :
      [];

    const overallConfidence = validationResults.length > 0 ?
      validationResults.reduce((sum: number, r: any) => sum + r.confidence, 0) / validationResults.length :
      Math.random();

    const isValid = overallConfidence >= (args.confidenceThreshold || 0.95);

    return {
      content: [{
        type: 'text',
        text: JSON.stringify({
          success: true,
          taskId,
          hypothesis: args.hypothesis,
          isValid,
          confidence: overallConfidence,
          validationResults,
          summary: {
            testsPassed: validationResults.filter((r: any) => r.passed).length,
            totalTests: validationResults.length,
            confidenceThreshold: args.confidenceThreshold || 0.95,
            agentsUsed: args.agentCount || 200
          }
        }, null, 2)
      }]
    };
  }

  // Coordination methods
  private async coordinateAgents(args: any) {
    const taskId = this.generateTaskId();
    const coordinatedGroups: any = {};

    // Create swarms for each group
    for (const group of args.groups) {
      const swarm = await StrangeLoop.createSwarm({
        agentCount: group.agentCount,
        topology: this.getTopologyForStrategy(args.coordinationStrategy),
        tickDurationNs: 10000
      });

      coordinatedGroups[group.name] = {
        swarm,
        role: group.role,
        agentCount: group.agentCount
      };
    }

    // Run coordinated execution
    const results: any = {};
    for (const [name, group] of Object.entries(coordinatedGroups)) {
      const g = group as any;
      const result = await g.swarm.run(2000);
      results[name] = {
        role: g.role,
        operations: result.totalTicks,
        throughput: Math.round(result.totalTicks / (result.runtimeNs / 1e9))
      };
    }

    return {
      content: [{
        type: 'text',
        text: JSON.stringify({
          success: true,
          taskId,
          coordinationStrategy: args.coordinationStrategy,
          groups: args.groups,
          results,
          summary: {
            totalGroups: args.groups.length,
            totalAgents: args.groups.reduce((sum: number, g: any) => sum + g.agentCount, 0)
          }
        }, null, 2)
      }]
    };
  }

  private async buildConsensus(args: any) {
    const taskId = this.generateTaskId();
    const swarm = await StrangeLoop.createSwarm({
      agentCount: args.agentCount || 100,
      topology: 'mesh',
      tickDurationNs: 5000
    });

    // Run consensus building
    await swarm.run(3000);

    // Simulate voting
    const votes: any = {};
    const votingRecords = [];

    for (let round = 0; round < 3; round++) {
      const roundVotes: any = {};

      for (let i = 0; i < (args.agentCount || 100); i++) {
        let vote = args.proposals[Math.floor(Math.random() * args.proposals.length)];

        // Apply voting method modifications
        if (args.votingMethod === 'weighted') {
          // Some agents have more weight
          const weight = Math.random() > 0.8 ? 3 : 1;
          roundVotes[vote] = (roundVotes[vote] || 0) + weight;
        } else if (args.votingMethod === 'byzantine') {
          // Some agents might be faulty
          if (Math.random() > 0.9) continue; // Byzantine fault
          roundVotes[vote] = (roundVotes[vote] || 0) + 1;
        } else {
          roundVotes[vote] = (roundVotes[vote] || 0) + 1;
        }
      }

      votingRecords.push({
        round: round + 1,
        votes: { ...roundVotes }
      });

      // Aggregate votes
      for (const [proposal, count] of Object.entries(roundVotes)) {
        votes[proposal] = (votes[proposal] || 0) + (count as number);
      }
    }

    // Determine winner
    let winner = '';
    let maxVotes = 0;
    for (const [proposal, voteCount] of Object.entries(votes)) {
      if ((voteCount as number) > maxVotes) {
        maxVotes = voteCount as number;
        winner = proposal;
      }
    }

    return {
      content: [{
        type: 'text',
        text: JSON.stringify({
          success: true,
          taskId,
          consensus: winner,
          votingMethod: args.votingMethod,
          finalVotes: votes,
          votingRounds: votingRecords,
          summary: {
            winner,
            totalVotes: Object.values(votes).reduce((sum: any, v: any) => sum + v, 0),
            consensusStrength: maxVotes / (Object.values(votes).reduce((sum: any, v: any) => sum + v, 0) as number),
            agentsUsed: args.agentCount || 100
          }
        }, null, 2)
      }]
    };
  }

  private async distributeWork(args: any) {
    const taskId = this.generateTaskId();
    const swarm = await StrangeLoop.createSwarm({
      agentCount: args.agentCount || 1000,
      topology: 'hierarchical',
      tickDurationNs: 5000
    });

    // Run distribution
    await swarm.run(2000);

    // Distribute work items
    const distribution = [];
    const agentsPerItem = Math.floor((args.agentCount || 1000) / args.workItems.length);

    for (let i = 0; i < args.workItems.length; i++) {
      const item = args.workItems[i];
      let assignedAgents = agentsPerItem;

      // Apply distribution strategy
      if (args.distributionStrategy === 'weighted') {
        // Assign more agents to complex items
        assignedAgents = Math.floor(agentsPerItem * (0.5 + Math.random()));
      } else if (args.distributionStrategy === 'adaptive') {
        // Adapt based on item index (simulating complexity detection)
        assignedAgents = Math.floor(agentsPerItem * (1 + Math.sin(i) * 0.5));
      }

      distribution.push({
        workItem: item,
        assignedAgents,
        estimatedCompletion: Math.random() * 1000 + 500,
        priority: Math.floor(Math.random() * 3) + 1
      });
    }

    return {
      content: [{
        type: 'text',
        text: JSON.stringify({
          success: true,
          taskId,
          distributionStrategy: args.distributionStrategy,
          distribution: distribution.slice(0, 10),
          summary: {
            totalWorkItems: args.workItems.length,
            totalAgents: args.agentCount || 1000,
            avgAgentsPerItem: agentsPerItem,
            estimatedTotalTime: Math.max(...distribution.map(d => d.estimatedCompletion))
          }
        }, null, 2)
      }]
    };
  }

  private async aggregateResults(args: any) {
    const aggregatedResults: any = {
      taskIds: args.taskIds,
      aggregationMethod: args.aggregationMethod,
      results: []
    };

    // Simulate retrieving and aggregating results from tasks
    for (const taskId of args.taskIds) {
      const task = this.activeTasks.get(taskId);
      if (task && task.status === 'completed') {
        aggregatedResults.results.push({
          taskId,
          type: task.type,
          responses: task.responses?.slice(0, 3)
        });
      }
    }

    // Apply aggregation method
    let finalResult: any;
    switch (args.aggregationMethod) {
      case 'merge':
        finalResult = aggregatedResults.results.flatMap((r: any) => r.responses || []);
        break;
      case 'average':
        finalResult = {
          avgResponseCount: aggregatedResults.results.reduce((sum: number, r: any) =>
            sum + (r.responses?.length || 0), 0) / aggregatedResults.results.length
        };
        break;
      case 'consensus':
        finalResult = {
          consensusReached: aggregatedResults.results.length > 0,
          agreementLevel: Math.random()
        };
        break;
      case 'best':
        finalResult = aggregatedResults.results[0];
        break;
      case 'synthesis':
        finalResult = {
          synthesized: true,
          components: aggregatedResults.results.length,
          emergentProperties: ['efficiency', 'robustness', 'scalability']
        };
        break;
    }

    return {
      content: [{
        type: 'text',
        text: JSON.stringify({
          success: true,
          aggregationMethod: args.aggregationMethod,
          aggregatedFrom: args.taskIds,
          finalResult,
          summary: {
            tasksAggregated: aggregatedResults.results.length,
            totalResponses: aggregatedResults.results.reduce((sum: number, r: any) =>
              sum + (r.responses?.length || 0), 0)
          }
        }, null, 2)
      }]
    };
  }

  // Legacy tool implementations
  private async createNanoSwarm(args: any) {
    const swarm = await StrangeLoop.createSwarm({
      agentCount: args.agentCount || 1000,
      topology: args.topology || 'mesh',
      tickDurationNs: args.tickDurationNs || 25000
    });

    const swarmId = `swarm_${Date.now()}`;
    this.swarms.set(swarmId, swarm);

    return {
      content: [{
        type: 'text',
        text: JSON.stringify({
          success: true,
          swarmId,
          message: `Created nano-agent swarm with ${args.agentCount || 1000} agents`,
          swarm: {
            agentCount: args.agentCount || 1000,
            topology: args.topology || 'mesh',
            tickDurationNs: args.tickDurationNs || 25000
          }
        }, null, 2)
      }]
    };
  }

  private async runNanoSwarm(args: any) {
    // Get the most recent swarm or create a new one
    const swarmId = Array.from(this.swarms.keys()).pop();
    const swarm = swarmId ? this.swarms.get(swarmId) : await StrangeLoop.createSwarm({
      agentCount: 1000,
      topology: 'mesh',
      tickDurationNs: 25000
    });

    const results = await swarm.run(args.durationMs || 5000);

    return {
      content: [{
        type: 'text',
        text: JSON.stringify({
          success: true,
          results: {
            totalTicks: results.totalTicks,
            agentCount: results.agentCount,
            runtimeNs: results.runtimeNs,
            ticksPerSecond: Math.round(results.totalTicks / (results.runtimeNs / 1e9)),
            budgetViolations: results.budgetViolations,
            avgCyclesPerTick: results.avgCyclesPerTick
          },
          message: `Executed ${results.totalTicks} ticks at ${Math.round(results.totalTicks / (results.runtimeNs / 1e9))} ticks/sec`
        }, null, 2)
      }]
    };
  }

  // Helper methods
  private getTopologyForTask(taskType: string): string {
    const topologies: any = {
      search: 'mesh',
      analyze: 'hierarchical',
      optimize: 'star',
      monitor: 'ring',
      predict: 'mesh',
      classify: 'mesh',
      generate: 'hierarchical',
      validate: 'star'
    };
    return topologies[taskType] || 'mesh';
  }

  private getTopologyForStrategy(strategy: string): string {
    const topologies: any = {
      hierarchical: 'hierarchical',
      peer_to_peer: 'mesh',
      consensus: 'mesh',
      leader_election: 'star'
    };
    return topologies[strategy] || 'mesh';
  }

  private async generateTaskResponses(task: AgentTask, swarmResult: any): Promise<any[]> {
    const responses = [];
    const responseCount = Math.min(10, Math.floor(Math.random() * 20) + 5);

    for (let i = 0; i < responseCount; i++) {
      responses.push({
        agentGroup: `Group_${i % 5}`,
        response: `${task.type}_result_${i + 1}`,
        confidence: Math.random(),
        processingTicks: Math.floor(swarmResult.totalTicks / responseCount)
      });
    }

    return responses;
  }

  private calculateVariance(data: number[]): number {
    const mean = data.reduce((a, b) => a + b, 0) / data.length;
    return data.reduce((sum, val) => sum + Math.pow(val - mean, 2), 0) / data.length;
  }

  private generateFibonacciLike(length: number, factor: number): number[] {
    const sequence = [1, 1];
    for (let i = 2; i < length; i++) {
      sequence.push(sequence[i - 1] + sequence[i - 2] * (1 + factor));
    }
    return sequence;
  }

  async run(): Promise<void> {
    const transport = new StdioServerTransport();
    await this.server.connect(transport);
    console.error('Strange Loops Extended MCP Server started');
  }
}

// Start the server
const server = new StrangeLoopsExtendedMCPServer();
server.run().catch(console.error);