Problem Solving MCP Server

An intelligent problem-solving server that automatically forms multi-role teams and uses the Eisenhower matrix for efficient task management and collaboration.

Multi-Role Collaborative Problem Solving Framework Based on Model Context Protocol

This is an intelligent problem-solving MCP server that creates3-12 professional rolesbased on problem complexity, uses theEisenhower Matrixfor priority management, and implementsparallel processing optimizationto generate comprehensive, executable, and efficient solutions.

- 🎭Intelligent Team Configuration: Automatically recommend 3-12 member teams based on problem complexity
- 🔍Multi-dimensional Quality Assurance: Comprehensive checks on completeness, feasibility, quality, risk, and timeline
- ⚡Parallel Processing Optimization: Automatically detect repetitive tasks and expand teams (up to 30 members)
- 📊Eisenhower Matrix Analysis: Important-urgent quadrant analysis for priority management
- 🤝Multi-role Collaboration: 12 professional role types for comprehensive problem solving
- 💡Reflection and Improvement: Built-in reflection mechanism for continuous optimization

{ "mcpServers": { "problem-solving": { "command": "node", "args": ["/path/to/problem-solving-mcp/dist/index.js"], "cwd": "/path/to/problem-solving-mcp", "env": { "NODE_ENV": "production" } } } }

{ "mcpServers": { "problem-solving": { "command": "node", "args": ["/path/to/problem-solving-mcp/dist/index.js"], "cwd": "/path/to/problem-solving-mcp", "env": { "NODE_ENV": "production" } } } }

Note: Replace/path/to/problem-solving-mcpwith your actual project path

graph TB A[Problem Input] --> B[Role Creator] B --> C[Team Assembly] C --> D[Solution Generation] D --> E[Result Checker] E --> F{Quality Check} F -->|Pass| G[Execution Plan] F -->|Fail| H[Improvement Suggestions] H --> D G --> I[Parallel Optimizer] I --> J[Team Expansion] J --> K[Parallel Execution] K --> L[Coordinator] L --> M[Final Solution] M --> N[Reflection & Learning] subgraph "Core Components" B E L I end subgraph "Quality Assurance" F H N end subgraph "Execution Optimization" I J K end

- Function: Intelligently create professional teams based on problem characteristics
- Team Size: 3-12 members (expandable to 30 for parallel processing)
- Role Types: 12 professional roles including analyst, researcher, designer, developer, etc.
- Smart Matching: Select core and supporting roles based on problem domain and complexity

- Multi-dimensional Assessment: Completeness, feasibility, quality, risk, timeline
- Problem Identification: Classify issues by severity (low, medium, high, critical)
- Improvement Suggestions: Generate specific, actionable recommendations
- Scoring System: Comprehensive scoring (0-100) with approval decisions

- Process Management: Complete problem-solving workflow orchestration
- Task Dependencies: Manage task relationships and parallel execution
- Progress Tracking: Real-time monitoring of solution progress
- Quality Control: Multi-round improvement and iteration support

4. Parallel Optimizer (parallel-optimizer.ts)

- Task Analysis: Evaluate task repetitiveness and workload
- Team Expansion: Intelligent scaling based on workload analysis
- Role Subdivision: Single-function multi-role parallel processing
- Efficiency Target: 2.5x performance improvement goal

// Good example { title: "Develop AI Customer Service System", description: "Develop intelligent customer service system for e-commerce platform, supporting multi-turn dialogue, sentiment analysis, and automatic replies", domain: "software_development", complexity_score: 8 }

- Simple Problems (1-3): 3-5 members, core roles
- Medium Problems (4-6): 6-8 members, core + supporting roles
- Complex Problems (7-10): 9-12 members, full professional team

Use Eisenhower Matrix for task prioritization:

- Urgent & Important: Immediate action
- Important & Not Urgent: Planned execution
- Urgent & Not Important: Delegate or automate
- Not Urgent & Not Important: Eliminate or postpone

NODE_ENV=production # Production mode DEBUG_MODE=false # Debug mode MAX_TEAM_SIZE=30 # Maximum team size PARALLEL_THRESHOLD=0.7 # Parallel processing threshold

// Extend role types in types.ts export enum RoleType { // ... existing roles custom_specialist = 'custom_specialist' }

- Team Expansion: Up to 30 members for complex tasks
- Parallel Processing: 2.5x efficiency improvement target
- Quality Assurance: Multi-dimensional scoring system
- Iteration Optimization: Reflection-based continuous improvement

- Capability-based: Workload distribution based on role capabilities
- Conflict Avoidance: Prevent resource conflicts
- Dynamic Load Balancing: Real-time workload adjustment

# Basic functionality test npm test # Integration test npm run test:integration # Performance test npm run test:performance

# Build project npm run build # Start service npm start # Process management (PM2) pm2 start dist/index.js --name problem-solving-mcp

- Health Checks: Service status monitoring
- Performance Metrics: Response time, success rate tracking
- Error Logging: Comprehensive error logging and alerting

- Horizontal Scaling: Multiple service instances
- Load Balancing: Request distribution
- Resource Monitoring: CPU, memory usage tracking

- 📧 Email:your-email@example.com
- 🐛 Issue Reporting:GitHub Issues
- 📖 Documentation:Wiki
- 💬 Community:Discord

- 🔧 Code Contributions: Follow ourContributing Guide
- 📝 Documentation: Help improve documentation
- 🐛 Bug Reports: Report issues with detailed information
- 💡 Feature Requests: Suggest new features

MIT License - seeLICENSEfile for details

Official Taskeract MCP Server for integrating your Taskeract project tasks and load the context of your tasks into your MCP enabled app.