/**
 * MCP Tools for Temporal Lead Solver
 * Provides temporal computational lead calculations through MCP
 */

import { Tool } from '@modelcontextprotocol/sdk/types.js';
import { TemporalPredictor } from 'temporal-lead-solver';

export class TemporalTools {
  private predictor: TemporalPredictor;

  constructor() {
    this.predictor = new TemporalPredictor(1e-6, 1000);
  }

  /**
   * Get all temporal lead tools
   */
  getTools(): Tool[] {
    return [
      {
        name: 'predictWithTemporalAdvantage',
        description: 'Predict solution with temporal computational lead - solve before data arrives',
        inputSchema: {
          type: 'object',
          properties: {
            matrix: {
              type: 'object',
              properties: {
                rows: { type: 'number', description: 'Number of rows' },
                cols: { type: 'number', description: 'Number of columns' },
                data: {
                  oneOf: [
                    {
                      type: 'array',
                      items: { type: 'array', items: { type: 'number' } },
                      description: 'Dense matrix format'
                    },
                    {
                      type: 'object',
                      properties: {
                        values: { type: 'array', items: { type: 'number' } },
                        rowIndices: { type: 'array', items: { type: 'number' } },
                        colIndices: { type: 'array', items: { type: 'number' } }
                      },
                      description: 'COO sparse format'
                    }
                  ]
                }
              },
              required: ['rows', 'cols', 'data'],
              description: 'Input matrix (must be diagonally dominant)'
            },
            vector: {
              type: 'array',
              items: { type: 'number' },
              description: 'Right-hand side vector b'
            },
            distanceKm: {
              type: 'number',
              description: 'Distance in kilometers for temporal advantage calculation',
              default: 10900
            }
          },
          required: ['matrix', 'vector']
        }
      },
      {
        name: 'validateTemporalAdvantage',
        description: 'Validate temporal computational lead for a given problem size',
        inputSchema: {
          type: 'object',
          properties: {
            size: {
              type: 'number',
              description: 'Matrix size to test',
              default: 1000,
              minimum: 10,
              maximum: 100000
            },
            distanceKm: {
              type: 'number',
              description: 'Distance in kilometers (default: Tokyo to NYC)',
              default: 10900
            }
          }
        }
      },
      {
        name: 'calculateLightTravel',
        description: 'Calculate light travel time vs computation time',
        inputSchema: {
          type: 'object',
          properties: {
            distanceKm: {
              type: 'number',
              description: 'Distance in kilometers',
              minimum: 0
            },
            matrixSize: {
              type: 'number',
              description: 'Size of the problem',
              default: 1000
            }
          },
          required: ['distanceKm']
        }
      },
      {
        name: 'demonstrateTemporalLead',
        description: 'Demonstrate temporal lead for various scenarios',
        inputSchema: {
          type: 'object',
          properties: {
            scenario: {
              type: 'string',
              enum: ['trading', 'satellite', 'network', 'custom'],
              description: 'Scenario to demonstrate',
              default: 'trading'
            },
            customDistance: {
              type: 'number',
              description: 'Custom distance in km (for custom scenario)'
            }
          }
        }
      }
    ];
  }

  /**
   * Handle tool calls
   */
  async handleToolCall(name: string, args: any): Promise<any> {
    switch (name) {
      case 'predictWithTemporalAdvantage':
        return this.predictWithTemporalAdvantage(args);

      case 'validateTemporalAdvantage':
        return this.validateTemporalAdvantage(args);

      case 'calculateLightTravel':
        return this.calculateLightTravel(args);

      case 'demonstrateTemporalLead':
        return this.demonstrateTemporalLead(args);

      default:
        throw new Error(`Unknown temporal tool: ${name}`);
    }
  }

  /**
   * Predict with temporal advantage
   */
  private async predictWithTemporalAdvantage(args: any): Promise<any> {
    const { matrix, vector, distanceKm = 10900 } = args;

    // Convert matrix to dense format if needed
    const denseMatrix = this.convertToDenseMatrix(matrix);

    // Calculate temporal advantage
    const result = this.predictor.predictWithTemporalAdvantage(
      denseMatrix,
      vector,
      distanceKm
    );

    return {
      solution: result.solution,
      computeTimeMs: result.computeTimeMs,
      lightTravelTimeMs: result.lightTravelTimeMs,
      temporalAdvantageMs: result.temporalAdvantageMs,
      effectiveVelocity: `${result.effectiveVelocityRatio.toFixed(0)}× speed of light`,
      queryCount: result.queryCount,
      sublinear: result.queryCount < vector.length / 2,
      summary: `Computed solution ${result.temporalAdvantageMs.toFixed(1)}ms before light could travel ${distanceKm}km`
    };
  }

  /**
   * Validate temporal advantage
   */
  private async validateTemporalAdvantage(args: any): Promise<any> {
    const { size = 1000, distanceKm = 10900 } = args;

    const validation = this.predictor.validateTemporalAdvantage(size);

    return {
      ...validation,
      interpretation: this.interpretResults(validation)
    };
  }

  /**
   * Calculate light travel comparison
   */
  private async calculateLightTravel(args: any): Promise<any> {
    const { distanceKm, matrixSize = 1000 } = args;

    const SPEED_OF_LIGHT_MPS = 299792458;
    const lightTravelTimeMs = (distanceKm * 1000) / (SPEED_OF_LIGHT_MPS / 1000);

    // Estimate computation time based on matrix size
    const estimatedComputeTime = Math.log2(matrixSize) * 0.1; // Sublinear estimate

    return {
      distance: {
        km: distanceKm,
        miles: distanceKm * 0.621371
      },
      lightTravelTime: {
        ms: lightTravelTimeMs,
        seconds: lightTravelTimeMs / 1000
      },
      estimatedComputeTime: {
        ms: estimatedComputeTime,
        seconds: estimatedComputeTime / 1000
      },
      temporalAdvantage: {
        ms: lightTravelTimeMs - estimatedComputeTime,
        ratio: lightTravelTimeMs / estimatedComputeTime
      },
      feasible: estimatedComputeTime < lightTravelTimeMs,
      summary: `Light takes ${lightTravelTimeMs.toFixed(1)}ms, computation takes ${estimatedComputeTime.toFixed(3)}ms`
    };
  }

  /**
   * Demonstrate temporal lead scenarios
   */
  private async demonstrateTemporalLead(args: any): Promise<any> {
    const { scenario = 'trading', customDistance } = args;

    const scenarios = {
      trading: {
        name: 'High-Frequency Trading',
        route: 'Tokyo → New York',
        distanceKm: 10900,
        context: 'Financial markets arbitrage'
      },
      satellite: {
        name: 'Satellite Communication',
        route: 'Ground → GEO Satellite',
        distanceKm: 35786,
        context: 'Geostationary orbit communication'
      },
      network: {
        name: 'Global Network Routing',
        route: 'London → Sydney',
        distanceKm: 16983,
        context: 'Internet backbone optimization'
      },
      custom: {
        name: 'Custom Scenario',
        route: 'Point A → Point B',
        distanceKm: customDistance || 1000,
        context: 'User-defined distance'
      }
    };

    const selected = scenarios[scenario];

    // Generate test problem
    const size = 1000;
    const matrix = [];
    const vector = new Array(size).fill(1);

    for (let i = 0; i < size; i++) {
      matrix[i] = new Array(size).fill(0);
      matrix[i][i] = 4;
      if (i > 0) matrix[i][i - 1] = -1;
      if (i < size - 1) matrix[i][i + 1] = -1;
    }

    const result = this.predictor.predictWithTemporalAdvantage(
      matrix,
      vector,
      selected.distanceKm
    );

    return {
      scenario: selected.name,
      route: selected.route,
      context: selected.context,
      distance: `${selected.distanceKm} km`,
      lightTravelTime: `${result.lightTravelTimeMs.toFixed(1)} ms`,
      computationTime: `${result.computeTimeMs.toFixed(3)} ms`,
      temporalAdvantage: `${result.temporalAdvantageMs.toFixed(1)} ms`,
      effectiveVelocity: `${result.effectiveVelocityRatio.toFixed(0)}× speed of light`,
      queryComplexity: `O(√n) = ${result.queryCount} queries for n=${size}`,
      practicalApplication: this.getPracticalApplication(scenario, result.temporalAdvantageMs),
      scientificValidity: 'Based on sublinear-time algorithms (Kwok-Wei-Yang 2025)',
      disclaimer: 'This is computational lead via prediction, not faster-than-light communication'
    };
  }

  /**
   * Convert matrix to dense format
   */
  private convertToDenseMatrix(matrix: any): number[][] {
    const { rows, cols, data } = matrix;

    if (Array.isArray(data)) {
      // Already dense
      return data;
    }

    // Convert from sparse (COO) to dense
    const dense: number[][] = [];
    for (let i = 0; i < rows; i++) {
      dense[i] = new Array(cols).fill(0);
    }

    if (data.values && data.rowIndices && data.colIndices) {
      for (let k = 0; k < data.values.length; k++) {
        dense[data.rowIndices[k]][data.colIndices[k]] = data.values[k];
      }
    }

    return dense;
  }

  /**
   * Interpret validation results
   */
  private interpretResults(validation: any): string {
    if (validation.valid) {
      return `✅ Temporal advantage confirmed: ${validation.temporalAdvantageMs}ms lead achieved with ${validation.effectiveVelocity}`;
    } else {
      return `❌ No temporal advantage: computation time exceeds light travel time`;
    }
  }

  /**
   * Get practical application description
   */
  private getPracticalApplication(scenario: string, advantageMs: number): string {
    const applications = {
      trading: `Execute trades ${advantageMs.toFixed(0)}ms before competitors receive market data`,
      satellite: `Process satellite commands before signals reach orbit`,
      network: `Route packets optimally before congestion information arrives`,
      custom: `Complete computation ${advantageMs.toFixed(0)}ms before traditional methods`
    };

    return applications[scenario] || applications.custom;
  }
}