1647 lines
46 KiB
JavaScript
1647 lines
46 KiB
JavaScript
#!/usr/bin/env node
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/**
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* Strange Loops Extended MCP Server
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* Task-oriented agent tools for real-world problem solving
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*/
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import { Server } from '@modelcontextprotocol/sdk/server/index.js';
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import { StdioServerTransport } from '@modelcontextprotocol/sdk/server/stdio.js';
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import {
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CallToolRequestSchema,
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ListToolsRequestSchema,
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Tool,
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} from '@modelcontextprotocol/sdk/types.js';
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// Import our Strange Loop library
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const StrangeLoop = require('../lib/strange-loop.js');
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// Agent task storage
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interface AgentTask {
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id: string;
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type: string;
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agents: number;
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status: 'pending' | 'running' | 'completed' | 'failed';
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results?: any;
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startTime?: number;
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endTime?: number;
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responses?: Array<any>;
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}
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class StrangeLoopsExtendedMCPServer {
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private server: Server;
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private isInitialized: boolean = false;
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private activeTasks: Map<string, AgentTask> = new Map();
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private swarms: Map<string, any> = new Map();
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private taskCounter: number = 0;
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constructor() {
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this.server = new Server(
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{
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name: 'strange-loops-extended',
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version: '0.2.0',
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},
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{
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capabilities: {
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tools: {},
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},
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}
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);
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this.setupHandlers();
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}
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private setupHandlers(): void {
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// List available tools
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this.server.setRequestHandler(ListToolsRequestSchema, async () => {
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return {
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tools: this.getToolDefinitions()
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};
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});
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// Handle tool calls
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this.server.setRequestHandler(CallToolRequestSchema, async (request) => {
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await this.ensureInitialized();
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const { name, arguments: args } = request.params;
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try {
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switch (name) {
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// Task Execution Tools
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case 'agent_task_create':
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return await this.createAgentTask(args);
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case 'agent_task_execute':
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return await this.executeAgentTask(args);
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case 'agent_task_status':
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return await this.getTaskStatus(args);
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case 'agent_task_results':
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return await this.getTaskResults(args);
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// Specific Task Types
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case 'agent_search':
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return await this.performAgentSearch(args);
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case 'agent_analyze':
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return await this.performAgentAnalysis(args);
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case 'agent_optimize':
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return await this.performOptimization(args);
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case 'agent_monitor':
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return await this.performMonitoring(args);
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case 'agent_predict':
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return await this.performPrediction(args);
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case 'agent_classify':
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return await this.performClassification(args);
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case 'agent_generate':
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return await this.performGeneration(args);
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case 'agent_validate':
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return await this.performValidation(args);
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// Coordination Tools
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case 'agent_coordinate':
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return await this.coordinateAgents(args);
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case 'agent_consensus':
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return await this.buildConsensus(args);
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case 'agent_distribute':
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return await this.distributeWork(args);
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case 'agent_aggregate':
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return await this.aggregateResults(args);
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// Original tools (backward compatibility)
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case 'nano_swarm_create':
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return await this.createNanoSwarm(args);
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case 'nano_swarm_run':
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return await this.runNanoSwarm(args);
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default:
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throw new Error(`Unknown tool: ${name}`);
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}
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} catch (error: any) {
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return {
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content: [
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{
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type: 'text',
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text: `Error: ${error.message}`,
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}
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]
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};
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}
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});
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}
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private getToolDefinitions(): Tool[] {
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return [
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// Task Creation and Execution
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{
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name: 'agent_task_create',
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description: 'Create a new agent task with specific goals and parameters',
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inputSchema: {
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type: 'object',
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properties: {
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taskType: {
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type: 'string',
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description: 'Type of task',
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enum: ['search', 'analyze', 'optimize', 'monitor', 'predict', 'classify', 'generate', 'validate']
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},
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description: {
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type: 'string',
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description: 'Task description and goals'
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},
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agentCount: {
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type: 'number',
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description: 'Number of agents to assign',
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default: 100,
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minimum: 1,
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maximum: 10000
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},
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parameters: {
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type: 'object',
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description: 'Task-specific parameters'
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}
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},
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required: ['taskType', 'description']
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}
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},
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{
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name: 'agent_task_execute',
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description: 'Execute a created task and get results',
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inputSchema: {
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type: 'object',
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properties: {
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taskId: {
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type: 'string',
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description: 'Task ID to execute'
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},
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timeoutMs: {
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type: 'number',
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description: 'Execution timeout in milliseconds',
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default: 5000
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}
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},
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required: ['taskId']
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}
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},
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{
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name: 'agent_task_status',
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description: 'Get the status of a running task',
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inputSchema: {
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type: 'object',
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properties: {
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taskId: {
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type: 'string',
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description: 'Task ID to check'
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}
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},
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required: ['taskId']
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}
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},
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{
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name: 'agent_task_results',
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description: 'Retrieve results from completed tasks',
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inputSchema: {
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type: 'object',
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properties: {
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taskId: {
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type: 'string',
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description: 'Task ID to get results from'
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},
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format: {
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type: 'string',
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description: 'Result format',
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enum: ['summary', 'detailed', 'raw'],
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default: 'summary'
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}
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},
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required: ['taskId']
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}
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},
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// Specific Task Types
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{
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name: 'agent_search',
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description: 'Deploy agents to search for specific patterns or solutions',
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inputSchema: {
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type: 'object',
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properties: {
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query: {
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type: 'string',
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description: 'What to search for'
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},
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searchSpace: {
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type: 'object',
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description: 'Define the search space',
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properties: {
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type: {
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type: 'string',
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enum: ['text', 'numerical', 'pattern', 'solution']
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},
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dimensions: {
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type: 'number',
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default: 10
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}
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}
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},
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agentCount: {
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type: 'number',
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default: 1000
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},
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strategy: {
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type: 'string',
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enum: ['breadth_first', 'depth_first', 'random', 'quantum_enhanced'],
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default: 'quantum_enhanced'
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}
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},
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required: ['query']
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}
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},
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{
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name: 'agent_analyze',
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description: 'Analyze data or patterns using distributed agents',
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inputSchema: {
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type: 'object',
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properties: {
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data: {
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type: 'array',
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description: 'Data to analyze'
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},
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analysisType: {
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type: 'string',
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enum: ['statistical', 'pattern', 'anomaly', 'trend', 'correlation'],
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default: 'pattern'
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},
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agentCount: {
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type: 'number',
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default: 500
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}
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},
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required: ['data']
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}
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},
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{
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name: 'agent_optimize',
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description: 'Optimize a function or process using swarm intelligence',
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inputSchema: {
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type: 'object',
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properties: {
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objective: {
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type: 'string',
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description: 'Optimization objective'
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},
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constraints: {
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type: 'array',
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items: {
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type: 'string'
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},
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description: 'Optimization constraints'
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},
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dimensions: {
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type: 'number',
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default: 10
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},
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agentCount: {
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type: 'number',
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default: 2000
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},
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iterations: {
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type: 'number',
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default: 100
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}
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},
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required: ['objective']
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}
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},
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{
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name: 'agent_monitor',
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description: 'Deploy monitoring agents to track metrics and detect anomalies',
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inputSchema: {
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type: 'object',
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properties: {
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metrics: {
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type: 'array',
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items: {
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type: 'string'
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},
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description: 'Metrics to monitor'
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},
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thresholds: {
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type: 'object',
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description: 'Alert thresholds for each metric'
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},
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agentCount: {
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type: 'number',
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default: 100
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},
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intervalMs: {
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type: 'number',
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description: 'Monitoring interval in milliseconds',
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default: 1000
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}
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},
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required: ['metrics']
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}
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},
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{
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name: 'agent_predict',
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description: 'Use temporal prediction agents to forecast future states',
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inputSchema: {
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type: 'object',
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properties: {
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historicalData: {
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type: 'array',
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description: 'Historical data points'
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},
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horizonSteps: {
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type: 'number',
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description: 'How many steps ahead to predict',
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default: 10
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},
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agentCount: {
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type: 'number',
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default: 500
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},
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useQuantum: {
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type: 'boolean',
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default: true
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}
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},
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required: ['historicalData']
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}
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},
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{
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name: 'agent_classify',
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description: 'Classify data using distributed agent consensus',
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inputSchema: {
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type: 'object',
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properties: {
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data: {
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type: 'array',
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description: 'Data to classify'
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},
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categories: {
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type: 'array',
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items: {
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type: 'string'
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},
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description: 'Possible categories'
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},
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agentCount: {
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type: 'number',
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default: 300
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},
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consensusThreshold: {
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type: 'number',
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description: 'Consensus threshold (0-1)',
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default: 0.7
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}
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},
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required: ['data', 'categories']
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}
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},
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{
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name: 'agent_generate',
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description: 'Generate new solutions or content using creative agents',
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inputSchema: {
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type: 'object',
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properties: {
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prompt: {
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type: 'string',
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description: 'Generation prompt or requirements'
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},
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generationType: {
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type: 'string',
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enum: ['solution', 'pattern', 'sequence', 'structure'],
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default: 'solution'
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},
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agentCount: {
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type: 'number',
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default: 1000
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},
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diversityFactor: {
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type: 'number',
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description: 'How diverse should the generations be (0-1)',
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default: 0.5
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}
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},
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required: ['prompt']
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}
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},
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{
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name: 'agent_validate',
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description: 'Validate solutions or hypotheses using verification agents',
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inputSchema: {
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type: 'object',
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properties: {
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hypothesis: {
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type: 'string',
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description: 'Hypothesis or solution to validate'
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},
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testCases: {
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type: 'array',
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description: 'Test cases for validation'
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},
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agentCount: {
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type: 'number',
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default: 200
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},
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confidenceThreshold: {
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type: 'number',
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description: 'Required confidence level (0-1)',
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default: 0.95
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}
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},
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required: ['hypothesis']
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}
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},
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// Coordination Tools
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{
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name: 'agent_coordinate',
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description: 'Coordinate multiple agent groups for complex tasks',
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inputSchema: {
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type: 'object',
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properties: {
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groups: {
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type: 'array',
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items: {
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type: 'object',
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properties: {
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name: { type: 'string' },
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agentCount: { type: 'number' },
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role: { type: 'string' }
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}
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},
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description: 'Agent groups to coordinate'
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},
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coordinationStrategy: {
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type: 'string',
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enum: ['hierarchical', 'peer_to_peer', 'consensus', 'leader_election'],
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default: 'hierarchical'
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}
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},
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required: ['groups']
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}
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},
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{
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name: 'agent_consensus',
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description: 'Build consensus among agents for decision making',
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inputSchema: {
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type: 'object',
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properties: {
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proposals: {
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type: 'array',
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items: {
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type: 'string'
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},
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description: 'Proposals to evaluate'
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},
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agentCount: {
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type: 'number',
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default: 100
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},
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votingMethod: {
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type: 'string',
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enum: ['majority', 'weighted', 'ranked', 'byzantine'],
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default: 'majority'
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}
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},
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required: ['proposals']
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}
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},
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{
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name: 'agent_distribute',
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description: 'Distribute work across agent swarm',
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inputSchema: {
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type: 'object',
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properties: {
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workItems: {
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type: 'array',
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description: 'Work items to distribute'
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},
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agentCount: {
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type: 'number',
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default: 1000
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},
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distributionStrategy: {
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type: 'string',
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enum: ['even', 'weighted', 'dynamic', 'adaptive'],
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default: 'adaptive'
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}
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},
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required: ['workItems']
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}
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},
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{
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name: 'agent_aggregate',
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description: 'Aggregate results from multiple agent tasks',
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inputSchema: {
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type: 'object',
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properties: {
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taskIds: {
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type: 'array',
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items: {
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type: 'string'
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},
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description: 'Task IDs to aggregate results from'
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},
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aggregationMethod: {
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type: 'string',
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enum: ['merge', 'average', 'consensus', 'best', 'synthesis'],
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default: 'synthesis'
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}
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},
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required: ['taskIds']
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}
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}
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];
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}
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|
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// Implementation methods
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private async ensureInitialized(): Promise<void> {
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if (!this.isInitialized) {
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await StrangeLoop.init();
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this.isInitialized = true;
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}
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}
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private generateTaskId(): string {
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return `task_${++this.taskCounter}_${Date.now()}`;
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}
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|
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// Task Management
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private async createAgentTask(args: any) {
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const taskId = this.generateTaskId();
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const task: AgentTask = {
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id: taskId,
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type: args.taskType,
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agents: args.agentCount || 100,
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status: 'pending',
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startTime: Date.now()
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};
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this.activeTasks.set(taskId, task);
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// Create swarm for this task
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const swarm = await StrangeLoop.createSwarm({
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agentCount: task.agents,
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topology: this.getTopologyForTask(args.taskType),
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tickDurationNs: 10000
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});
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this.swarms.set(taskId, swarm);
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return {
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content: [{
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type: 'text',
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text: JSON.stringify({
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success: true,
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taskId,
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message: `Created ${args.taskType} task with ${task.agents} agents`,
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task: {
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id: taskId,
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type: task.type,
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agents: task.agents,
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status: task.status,
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parameters: args.parameters
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}
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}, null, 2)
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}]
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};
|
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}
|
|
|
|
private async executeAgentTask(args: any) {
|
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const task = this.activeTasks.get(args.taskId);
|
|
if (!task) {
|
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throw new Error(`Task ${args.taskId} not found`);
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}
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|
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const swarm = this.swarms.get(args.taskId);
|
|
if (!swarm) {
|
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throw new Error(`Swarm for task ${args.taskId} not found`);
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}
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|
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task.status = 'running';
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|
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// Run the swarm
|
|
const result = await swarm.run(args.timeoutMs || 5000);
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|
|
// Generate task-specific results
|
|
const responses = await this.generateTaskResponses(task, result);
|
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|
|
task.status = 'completed';
|
|
task.endTime = Date.now();
|
|
task.results = result;
|
|
task.responses = responses;
|
|
|
|
return {
|
|
content: [{
|
|
type: 'text',
|
|
text: JSON.stringify({
|
|
success: true,
|
|
taskId: args.taskId,
|
|
status: 'completed',
|
|
executionTimeMs: task.endTime - task.startTime!,
|
|
summary: {
|
|
totalOperations: result.totalTicks,
|
|
throughput: `${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); |