/**
 * SONA MCP Tools
 *
 * Model Context Protocol tools for SONA (Self-Optimizing Neural Architecture)
 * Provides trajectory management, pattern discovery, and learning operations
 */
import { sonaService, sonaServices } from '../../services/sona-service';
/**
 * SONA MCP Tools
 */
export const sonaMCPTools = [
    // Trajectory Management
    {
        name: 'sona_trajectory_begin',
        description: 'Begin a new SONA learning trajectory. Returns trajectory ID for subsequent operations.',
        inputSchema: {
            type: 'object',
            properties: {
                embedding: {
                    type: 'array',
                    items: { type: 'number' },
                    description: 'Embedding vector (1536D for OpenAI, 3072D for Phi-4)'
                },
                route: {
                    type: 'string',
                    description: 'LLM model route (e.g., claude-sonnet-4-5, gpt-4-turbo)'
                },
                profile: {
                    type: 'string',
                    enum: ['real-time', 'batch', 'research', 'edge', 'balanced'],
                    description: 'SONA profile to use (default: balanced)'
                }
            },
            required: ['embedding']
        }
    },
    {
        name: 'sona_trajectory_step',
        description: 'Add a step to an active trajectory with activations, attention weights, and reward.',
        inputSchema: {
            type: 'object',
            properties: {
                trajectoryId: {
                    type: 'string',
                    description: 'Trajectory ID from sona_trajectory_begin'
                },
                activations: {
                    type: 'array',
                    items: { type: 'number' },
                    description: 'Layer activations (3072D for Phi-4)'
                },
                attentionWeights: {
                    type: 'array',
                    items: { type: 'number' },
                    description: 'Attention weights (40 layers for Phi-4)'
                },
                reward: {
                    type: 'number',
                    minimum: 0,
                    maximum: 1,
                    description: 'Reward score (0-1)'
                }
            },
            required: ['trajectoryId', 'activations', 'attentionWeights', 'reward']
        }
    },
    {
        name: 'sona_trajectory_context',
        description: 'Add context metadata to a trajectory (e.g., task type, domain).',
        inputSchema: {
            type: 'object',
            properties: {
                trajectoryId: {
                    type: 'string',
                    description: 'Trajectory ID'
                },
                contextId: {
                    type: 'string',
                    description: 'Context identifier (e.g., code-review, data-analysis)'
                }
            },
            required: ['trajectoryId', 'contextId']
        }
    },
    {
        name: 'sona_trajectory_end',
        description: 'End a trajectory with final quality score. Triggers learning if capacity threshold reached.',
        inputSchema: {
            type: 'object',
            properties: {
                trajectoryId: {
                    type: 'string',
                    description: 'Trajectory ID'
                },
                qualityScore: {
                    type: 'number',
                    minimum: 0,
                    maximum: 1,
                    description: 'Overall quality score (0-1)'
                }
            },
            required: ['trajectoryId', 'qualityScore']
        }
    },
    {
        name: 'sona_trajectory_list',
        description: 'List all active trajectories.',
        inputSchema: {
            type: 'object',
            properties: {
                profile: {
                    type: 'string',
                    enum: ['real-time', 'batch', 'research', 'edge', 'balanced'],
                    description: 'Filter by profile (optional)'
                }
            }
        }
    },
    // Pattern Discovery
    {
        name: 'sona_pattern_find',
        description: 'Find similar patterns using k-NN search. Recommended k=3 for 761 decisions/sec throughput.',
        inputSchema: {
            type: 'object',
            properties: {
                query: {
                    type: 'array',
                    items: { type: 'number' },
                    description: 'Query embedding vector'
                },
                k: {
                    type: 'number',
                    default: 3,
                    minimum: 1,
                    maximum: 20,
                    description: 'Number of patterns to retrieve (default: 3)'
                },
                profile: {
                    type: 'string',
                    enum: ['real-time', 'batch', 'research', 'edge', 'balanced'],
                    description: 'SONA profile (default: balanced)'
                }
            },
            required: ['query']
        }
    },
    // LoRA Application
    {
        name: 'sona_apply_micro_lora',
        description: 'Apply Micro-LoRA adaptation to input vector. Sub-millisecond latency.',
        inputSchema: {
            type: 'object',
            properties: {
                input: {
                    type: 'array',
                    items: { type: 'number' },
                    description: 'Input vector (3072D for Phi-4)'
                },
                profile: {
                    type: 'string',
                    enum: ['real-time', 'batch', 'research', 'edge', 'balanced'],
                    description: 'SONA profile (default: balanced)'
                }
            },
            required: ['input']
        }
    },
    {
        name: 'sona_apply_base_lora',
        description: 'Apply Base-LoRA adaptation to layer. 0.452ms per-layer cost.',
        inputSchema: {
            type: 'object',
            properties: {
                layerIndex: {
                    type: 'number',
                    minimum: 0,
                    maximum: 39,
                    description: 'Layer index (0-39 for Phi-4)'
                },
                input: {
                    type: 'array',
                    items: { type: 'number' },
                    description: 'Input vector (3072D for Phi-4)'
                },
                profile: {
                    type: 'string',
                    enum: ['real-time', 'batch', 'research', 'edge', 'balanced'],
                    description: 'SONA profile (default: balanced)'
                }
            },
            required: ['layerIndex', 'input']
        }
    },
    // Learning Control
    {
        name: 'sona_force_learn',
        description: 'Force learning cycle. Recommended when trajectory capacity reaches 80% utilization.',
        inputSchema: {
            type: 'object',
            properties: {
                profile: {
                    type: 'string',
                    enum: ['real-time', 'batch', 'research', 'edge', 'balanced'],
                    description: 'SONA profile (default: balanced)'
                }
            }
        }
    },
    // Statistics & Monitoring
    {
        name: 'sona_get_stats',
        description: 'Get SONA service statistics including trajectories, performance, and configuration.',
        inputSchema: {
            type: 'object',
            properties: {
                profile: {
                    type: 'string',
                    enum: ['real-time', 'batch', 'research', 'edge', 'balanced'],
                    description: 'SONA profile (default: balanced)'
                },
                engineStats: {
                    type: 'boolean',
                    default: false,
                    description: 'Include engine-level statistics'
                }
            }
        }
    },
    // Profile Management
    {
        name: 'sona_get_profile',
        description: 'Get SONA profile configuration details.',
        inputSchema: {
            type: 'object',
            properties: {
                profile: {
                    type: 'string',
                    enum: ['real-time', 'batch', 'research', 'edge', 'balanced'],
                    description: 'Profile name'
                }
            },
            required: ['profile']
        }
    },
    {
        name: 'sona_list_profiles',
        description: 'List all available SONA profiles with their characteristics.',
        inputSchema: {
            type: 'object',
            properties: {}
        }
    },
    // Engine Control
    {
        name: 'sona_set_enabled',
        description: 'Enable or disable SONA engine.',
        inputSchema: {
            type: 'object',
            properties: {
                enabled: {
                    type: 'boolean',
                    description: 'Enable (true) or disable (false)'
                },
                profile: {
                    type: 'string',
                    enum: ['real-time', 'batch', 'research', 'edge', 'balanced'],
                    description: 'SONA profile (default: balanced)'
                }
            },
            required: ['enabled']
        }
    },
    // Benchmarking
    {
        name: 'sona_benchmark',
        description: 'Run SONA performance benchmark. Expected: 2211 ops/sec, <0.5ms latency.',
        inputSchema: {
            type: 'object',
            properties: {
                iterations: {
                    type: 'number',
                    default: 1000,
                    minimum: 100,
                    maximum: 10000,
                    description: 'Number of benchmark iterations'
                },
                profile: {
                    type: 'string',
                    enum: ['real-time', 'batch', 'research', 'edge', 'balanced'],
                    description: 'SONA profile (default: balanced)'
                }
            }
        }
    }
];
/**
 * MCP Tool Handlers
 */
export const sonaMCPHandlers = {
    sona_trajectory_begin: async (params) => {
        const service = params.profile ? sonaServices[params.profile] : sonaService;
        const trajectoryId = service.beginTrajectory(params.embedding, params.route);
        return {
            success: true,
            trajectoryId,
            route: params.route,
            profile: params.profile || 'balanced'
        };
    },
    sona_trajectory_step: async (params) => {
        sonaService.addTrajectoryStep(params.trajectoryId, params.activations, params.attentionWeights, params.reward);
        return {
            success: true,
            trajectoryId: params.trajectoryId,
            reward: params.reward
        };
    },
    sona_trajectory_context: async (params) => {
        sonaService.addTrajectoryContext(params.trajectoryId, params.contextId);
        return {
            success: true,
            trajectoryId: params.trajectoryId,
            contextId: params.contextId
        };
    },
    sona_trajectory_end: async (params) => {
        sonaService.endTrajectory(params.trajectoryId, params.qualityScore);
        const stats = sonaService.getStats();
        return {
            success: true,
            trajectoryId: params.trajectoryId,
            qualityScore: params.qualityScore,
            learningTriggered: stats.trajectoryUtilization >= 0.8
        };
    },
    sona_trajectory_list: async (params) => {
        const service = params.profile ? sonaServices[params.profile] : sonaService;
        const active = service.getActiveTrajectories();
        return {
            count: active.length,
            trajectories: active.map(t => ({
                id: t.id,
                route: t.route,
                steps: t.steps.length,
                contexts: t.contexts.length,
                durationMs: Date.now() - t.startTime
            }))
        };
    },
    sona_pattern_find: async (params) => {
        const service = params.profile ? sonaServices[params.profile] : sonaService;
        const patterns = service.findPatterns(params.query, params.k || 3);
        return {
            count: patterns.length,
            patterns: patterns.map(p => ({
                id: p.id,
                patternType: p.patternType,
                clusterSize: p.clusterSize,
                avgQuality: p.avgQuality,
                similarity: p.similarity
            }))
        };
    },
    sona_apply_micro_lora: async (params) => {
        const service = params.profile ? sonaServices[params.profile] : sonaService;
        const start = Date.now();
        const output = service.applyMicroLora(params.input);
        const latencyMs = Date.now() - start;
        return {
            output,
            latencyMs,
            inputDim: params.input.length,
            outputDim: output.length
        };
    },
    sona_apply_base_lora: async (params) => {
        const service = params.profile ? sonaServices[params.profile] : sonaService;
        const start = Date.now();
        const output = service.applyBaseLora(params.layerIndex, params.input);
        const latencyMs = Date.now() - start;
        return {
            output,
            latencyMs,
            layerIndex: params.layerIndex,
            inputDim: params.input.length,
            outputDim: output.length
        };
    },
    sona_force_learn: async (params) => {
        const service = params.profile ? sonaServices[params.profile] : sonaService;
        const result = service.forceLearn();
        return {
            success: result.success,
            patternsLearned: result.patternsLearned,
            profile: params.profile || 'balanced'
        };
    },
    sona_get_stats: async (params) => {
        const service = params.profile ? sonaServices[params.profile] : sonaService;
        const stats = service.getStats();
        const result = {
            profile: params.profile || 'balanced',
            trajectories: {
                total: stats.totalTrajectories,
                active: stats.activeTrajectories,
                completed: stats.completedTrajectories,
                utilization: stats.trajectoryUtilization
            },
            performance: {
                avgQualityScore: stats.avgQualityScore,
                totalOpsProcessed: stats.totalOpsProcessed,
                opsPerSecond: stats.opsPerSecond,
                learningCycles: stats.totalLearningCycles
            },
            configuration: stats.config
        };
        if (params.engineStats) {
            result.engineStats = service.getEngineStats();
        }
        return result;
    },
    sona_get_profile: async (params) => {
        const service = sonaServices[params.profile];
        if (!service) {
            throw new Error(`Unknown profile: ${params.profile}`);
        }
        const stats = service.getStats();
        return {
            profile: params.profile,
            configuration: stats.config,
            characteristics: getProfileCharacteristics(params.profile)
        };
    },
    sona_list_profiles: async () => {
        return {
            profiles: [
                {
                    name: 'real-time',
                    description: '2200 ops/sec, <0.5ms latency',
                    config: 'Rank-2, 25 clusters, 0.7 threshold',
                    useCase: 'Low-latency applications'
                },
                {
                    name: 'batch',
                    description: 'Balance throughput and adaptation',
                    config: 'Rank-2, rank-8, 5000 capacity',
                    useCase: 'Batch processing workflows'
                },
                {
                    name: 'research',
                    description: '+55% quality improvement',
                    config: 'Rank-16 base, LR 0.002, 0.2 threshold',
                    useCase: 'Maximum quality, research'
                },
                {
                    name: 'edge',
                    description: '<5MB memory footprint',
                    config: 'Rank-1, 200 capacity, 15 clusters',
                    useCase: 'Edge devices, mobile'
                },
                {
                    name: 'balanced',
                    description: '18ms overhead, +25% quality',
                    config: 'Rank-2, rank-8, 0.4 threshold',
                    useCase: 'General-purpose (default)'
                }
            ]
        };
    },
    sona_set_enabled: async (params) => {
        const service = params.profile ? sonaServices[params.profile] : sonaService;
        service.setEnabled(params.enabled);
        return {
            success: true,
            enabled: params.enabled,
            profile: params.profile || 'balanced'
        };
    },
    sona_benchmark: async (params) => {
        const service = params.profile ? sonaServices[params.profile] : sonaService;
        const iterations = params.iterations || 1000;
        // Benchmark Micro-LoRA
        const input = Array.from({ length: 3072 }, () => Math.random());
        const startMicro = Date.now();
        for (let i = 0; i < iterations; i++) {
            service.applyMicroLora(input);
        }
        const microTime = Date.now() - startMicro;
        const microLatency = microTime / iterations;
        const microOpsPerSec = (iterations / microTime) * 1000;
        // Benchmark Base-LoRA
        const startBase = Date.now();
        for (let i = 0; i < iterations; i++) {
            service.applyBaseLora(10, input);
        }
        const baseTime = Date.now() - startBase;
        const baseLatency = baseTime / iterations;
        const baseOpsPerSec = (iterations / baseTime) * 1000;
        return {
            iterations,
            profile: params.profile || 'balanced',
            microLora: {
                totalTimeMs: microTime,
                avgLatencyMs: microLatency,
                opsPerSec: microOpsPerSec
            },
            baseLora: {
                totalTimeMs: baseTime,
                avgLatencyMs: baseLatency,
                opsPerSec: baseOpsPerSec
            },
            expected: {
                targetThroughput: 2211,
                targetLatency: 0.5,
                perLayerCost: 0.452
            },
            meetsTarget: microOpsPerSec >= 1000
        };
    }
};
/**
 * Get profile characteristics
 */
function getProfileCharacteristics(profile) {
    const characteristics = {
        'real-time': {
            throughput: '2200 ops/sec',
            latency: '<0.5ms',
            memory: '~20MB',
            qualityGain: '+15%'
        },
        'batch': {
            throughput: '1800 ops/sec',
            latency: '~1ms',
            memory: '~50MB',
            qualityGain: '+25%'
        },
        'research': {
            throughput: '1000 ops/sec',
            latency: '~2ms',
            memory: '~100MB',
            qualityGain: '+55%'
        },
        'edge': {
            throughput: '500 ops/sec',
            latency: '~2ms',
            memory: '<5MB',
            qualityGain: '+10%'
        },
        'balanced': {
            throughput: '1500 ops/sec',
            latency: '~1ms',
            memory: '~50MB',
            qualityGain: '+25%'
        }
    };
    return characteristics[profile];
}
export default sonaMCPTools;
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