90cigars/idwo-mcp-agent

Intelligent Development Workflow Orchestrator (IDWO)

An advanced MCP (Model Context Protocol) Server Agent that automates complex developer workflows by intelligently orchestrating actions across GitHub, JIRA, and Slack using AI-powered decision making.

🎯 Overview

IDWO bridges the gap between different development tools by providing intelligent automation that understands context, makes decisions, and executes multi-step workflows across platforms. Unlike simple webhook-based automations, IDWO uses LLM-powered analysis to provide contextual, adaptive workflow management.

Key Capabilities

• 🧠 AI-Powered Analysis: Uses GPT-4 to analyze pull requests, triage issues, and assess release readiness
• 🔄 Cross-Platform Orchestration: Seamlessly coordinates actions across GitHub, JIRA, and Slack
• 📊 Intelligent Insights: Generates team productivity metrics and identifies workflow bottlenecks
• 🎛️ MCP Integration: Works with ChatGPT, Claude, GitHub Copilot, and any MCP-compatible interface
• ⚡ Real-Time Workflow Sync: Maintains consistent state across all integrated platforms

🏗️ Architecture

graph TB
    A[MCP Client] --> B[IDWO MCP Server]
    B --> C[OpenAI Agent]
    B --> D[GitHub Integration]
    B --> E[JIRA Integration] 
    B --> F[Slack Integration]
    B --> G[Workflow Orchestrator]
    
    C --> H[GPT-4 Analysis]
    D --> I[GitHub API]
    E --> J[JIRA REST API]
    F --> K[Slack Web API]
    G --> L[State Management]

Core Components

• MCP Server: Exposes tools and handles client requests
• Service Integrations: GitHub, JIRA, and Slack API clients with error handling
• AI Agent: OpenAI integration for intelligent decision making
• Workflow Orchestrator: Coordinates multi-step operations across services
• State Management: Tracks workflow status and maintains consistency

🚀 Quick Start

Prerequisites

• Node.js 22+
• Docker and Docker Compose (optional)
• API keys for OpenAI, GitHub, JIRA, and Slack

Installation

1. Clone the repository

   git clone https://github.com/your-org/idwo-mcp-server.git
   cd idwo-mcp-server
   

2. Install dependencies

   npm install
   

3. Configure environment

   cp .env.example .env
   # Edit .env with your API keys and configuration
   

4. Build and start

   npm run build
   npm start
   

Docker Quick Start

# Start with all dependencies
docker-compose up -d

# Production deployment
docker-compose -f docker-compose.prod.yml up -d

🔧 Configuration

Environment Variables

Required API Keys

OPENAI_API_KEY=sk-your-openai-api-key-here
GITHUB_TOKEN=ghp_your-github-token-here
JIRA_URL=https://your-domain.atlassian.net
JIRA_USERNAME=your-email@company.com
JIRA_API_TOKEN=your-jira-api-token
SLACK_BOT_TOKEN=xoxb-your-slack-bot-token

Optional Configuration

# Database & Cache
DATABASE_URL=postgresql://user:password@localhost:5432/idwo
REDIS_URL=redis://localhost:6379

# Security
JWT_SECRET=your-jwt-secret-key
ENCRYPTION_KEY=your-32-character-encryption-key

# Performance
RATE_LIMIT_MAX_REQUESTS=100
CIRCUIT_BREAKER_TIMEOUT=5000

Service Setup

GitHub

1. Create a Personal Access Token with repo, read:org, and read:user permissions
2. Set GITHUB_TOKEN in environment variables

JIRA

1. Create an API token in Atlassian Account Settings
2. Configure JIRA_URL, JIRA_USERNAME, and JIRA_API_TOKEN

Slack

1. Create a Slack App with appropriate scopes (chat:write, channels:read, users:read)
2. Install the app to your workspace and obtain bot token
3. Set SLACK_BOT_TOKEN and other Slack configuration

OpenAI

1. Obtain an API key from OpenAI platform
2. Set OPENAI_API_KEY and optionally OPENAI_MODEL

📖 MCP Tools Reference

analyze_pr

Analyzes pull requests with AI-powered insights.

{
  "owner": "string",
  "repo": "string", 
  "pull_number": 123,
  "include_jira_context": true
}

Response: Risk assessment, suggested reviewers, estimated review time, related JIRA tickets

smart_triage

Automatically categorizes and prioritizes issues.

{
  "issue_key": "PROJ-123",
  "github_issue_url": "https://github.com/org/repo/issues/456",
  "team_context": "backend-team"
}

Response: Priority, category, effort estimate, suggested assignee and sprint

orchestrate_release

Coordinates releases across multiple platforms.

{
  "release_version": "v2.1.0",
  "repository": "org/repo",
  "jira_project": "PROJ",
  "slack_channel": "#releases",
  "dry_run": false
}

Response: Readiness score, blockers, test coverage, recommendations

sync_workflow_status

Synchronizes status updates across platforms.

{
  "workflow_id": "pr-org-repo-123",
  "status_update": "in-review",
  "platforms": ["github", "jira", "slack"]
}

get_team_insights

Generates productivity analytics and identifies bottlenecks.

{
  "team_name": "backend-team",
  "time_period": "30d",
  "include_predictions": true
}

Response: Velocity trends, bottlenecks, metrics, predictions

🧪 Testing

Unit Tests

npm run test
npm run test:watch

Integration Tests

npm run test:integration

Coverage Reports

npm run test -- --coverage

Test Structure

• Unit tests: Individual component functionality
• Integration tests: End-to-end workflow validation
• Mock services: External API simulation
• Error scenarios: Failure mode testing

🚢 Deployment

Local Development

npm run dev  # Development with hot reload

Docker Deployment

# Build production image
docker build -t idwo-mcp-server .

# Run with docker-compose
docker-compose up -d

Kubernetes Deployment

# Apply manifests
kubectl apply -f deployment/k8s/

# Or using Helm
helm install idwo deployment/helm/idwo-chart/ \
  --set secrets.openai.apiKey=$OPENAI_API_KEY \
  --set secrets.github.token=$GITHUB_TOKEN

Production Considerations

• Use managed databases (RDS, Redis ElastiCache)
• Configure proper secrets management (Kubernetes secrets, HashiCorp Vault)
• Set up monitoring and alerting (Prometheus, Grafana)
• Implement proper logging aggregation (ELK stack)
• Configure load balancers and ingress controllers

🔒 Security Considerations

Current Security Measures

• Authentication: OAuth 2.0 for all service integrations
• Encryption: Sensitive data encrypted at rest and in transit
• Rate Limiting: Request throttling to prevent abuse
• Input Validation: Zod schemas for all inputs
• Network Security: Docker network isolation, Kubernetes network policies
• Container Security: Non-root user, read-only filesystem, minimal attack surface

Potential Security Risks & Mitigations

Security Best Practices

1. Secret Management: Use external secret stores (Vault, AWS Secrets Manager)
2. Access Control: Implement RBAC for different user roles
3. Audit Logging: Log all API calls and workflow executions
4. Network Security: Use VPCs, security groups, and network policies
5. Regular Updates: Keep dependencies updated and scan for vulnerabilities

⚡ Performance & Scalability

Current Performance Characteristics

• Response Time: Sub-second for simple operations, 2-5s for AI analysis
• Throughput: 100+ requests/minute per instance
• Concurrency: Async/await throughout, connection pooling
• Memory Usage: ~512MB baseline, 2GB under load
• CPU Usage: 0.1-0.5 CPU cores typical, 1+ during AI inference

Scaling Strategies

Horizontal Scaling

• Kubernetes HPA: CPU/memory-based autoscaling (2-10 pods)
• Load Balancing: Round-robin across healthy instances
• Stateless Design: No local state dependencies

Performance Optimizations

• Connection Pooling: Persistent HTTP connections to APIs
• Caching: Redis for API responses and computed results
• Circuit Breakers: Fail-fast for unhealthy services
• Batching: Group operations where possible

Bottleneck Analysis

Monitoring Metrics

• Request latency percentiles (p50, p95, p99)
• Error rates by service and operation
• API rate limit utilization
• Database connection pool usage
• Memory and CPU utilization

🐛 Failure Scenarios & Testing

Common Failure Modes

API Service Failures

• GitHub API Outage: Circuit breaker prevents cascade failures
• JIRA Rate Limiting: Request queuing and backoff strategies
• Slack Service Degradation: Graceful degradation with logging
• OpenAI Timeouts: Retry logic with exponential backoff

Infrastructure Failures

• Database Connectivity: Connection pooling with automatic reconnection
• Redis Cache Miss: Fallback to direct API calls
• Network Partitions: Service mesh resilience patterns
• Pod Failures: Kubernetes automatic restart and scaling

Testing Strategies

Chaos Engineering

# Simulate service failures
kubectl patch deployment idwo-server -p '{"spec":{"template":{"spec":{"containers":[{"name":"idwo-server","env":[{"name":"GITHUB_TOKEN","value":"invalid"}]}]}}}}'

# Network partitioning
kubectl apply -f tests/chaos/network-partition.yaml

# Resource constraints
kubectl apply -f tests/chaos/memory-pressure.yaml

Load Testing

# Install k6 and run load tests
k6 run tests/load/pr-analysis.js
k6 run tests/load/workflow-orchestration.js

Integration Test Scenarios

• API authentication failures
• Malformed API responses
• Timeout and retry logic
• Rate limit handling
• Data consistency checks

Recovery Procedures

1. Service Recovery: Automatic restart via health checks
2. Data Recovery: Point-in-time database restoration
3. State Reconciliation: Workflow state validation and repair
4. Monitoring: Automated alerting and incident response

📈 Monitoring & Observability

Metrics Collection

• Application Metrics: Request rates, response times, error counts
• Business Metrics: Workflow completion rates, AI accuracy
• Infrastructure Metrics: CPU, memory, network, disk usage
• Custom Metrics: API quota usage, cache hit rates

Logging Strategy

• Structured Logging: JSON format with correlation IDs
• Log Levels: Error, warn, info, debug with appropriate filtering
• Centralized Collection: ELK stack or cloud logging services
• Log Retention: 30 days for debug, 1 year for audit logs

Alerting Rules

• High error rates (>5% for 5 minutes)
• Response time degradation (p95 > 10s)
• API rate limit consumption (>80%)
• Failed workflow orchestrations
• Infrastructure resource exhaustion

Dashboards

• Operations Dashboard: System health, request metrics, error rates
• Business Dashboard: Workflow completion, user adoption, ROI metrics
• Performance Dashboard: Response times, throughput, resource utilization

🔮 Future Roadmap

Near-term Improvements (3-6 months)

Multi-tenant Architecture

• Organization Isolation: Separate data and configurations per tenant
• RBAC Implementation: Role-based access control within organizations
• Billing Integration: Usage tracking and cost allocation

Enhanced Analytics

• Predictive Analytics: ML models for sprint planning and risk assessment
• Anomaly Detection: Unusual patterns in team behavior or system performance
• Custom Metrics: User-defined KPIs and dashboard customization

Medium-term Expansion (6-12 months)

Additional Integrations

• Linear: Issue tracking for modern development teams
• Notion: Documentation and knowledge management
• PagerDuty: Incident management and alerting
• Discord: Team communication and notifications

Advanced AI Capabilities

• Custom Model Fine-tuning: Organization-specific AI models
• Multi-modal Analysis: Code, documentation, and visual content
• Automated Code Review: AI-powered code quality assessment

Workflow Builder

• Visual Editor: Drag-and-drop workflow creation
• Template Library: Pre-built automation patterns
• Custom Actions: User-defined workflow steps

Long-term Vision (12+ months)

Multi-cloud Deployment

• Cloud Agnostic: AWS, GCP, Azure deployment options
• Edge Computing: Regional deployments for low latency
• Hybrid Cloud: On-premises and cloud hybrid architectures

Advanced Analytics Platform

• Data Lake Integration: Historical data analysis and reporting
• Real-time Streaming: Live workflow monitoring and alerts
• ML Pipeline: Automated model training and deployment

Platform Ecosystem

• Plugin Architecture: Third-party integrations and extensions
• API Marketplace: Community-contributed workflow templates
• Developer SDK: Tools for building custom integrations

📚 Architecture Decisions & Tradeoffs

Technology Stack Decisions

TypeScript vs Python

Chosen: TypeScript

• ✅ Pros: Superior async handling, shared frontend/backend code, better MCP ecosystem
• ❌ Cons: Smaller ML ecosystem, more complex build process
• Alternative: Python with FastMCP for data-heavy workflows

Direct API Integration vs Webhook Architecture

Chosen: Direct API Integration with Circuit Breakers

• ✅ Pros: Real-time data, fine-grained control, easier testing
• ❌ Cons: Higher API consumption, potential polling overhead
• Alternative: Event-driven webhooks for high-volume scenarios

Monolithic vs Microservices

Chosen: Modular Monolith

• ✅ Pros: Simpler deployment, shared types, easier debugging
• ❌ Cons: Scaling limitations, technology coupling
• Future: Microservices transition as teams and complexity grow

Infrastructure Decisions

Kubernetes vs Serverless

Chosen: Kubernetes with Serverless Options

• ✅ Pros: Better resource utilization, service mesh benefits, vendor neutrality
• ❌ Cons: Operational complexity, higher infrastructure costs
• Alternative: AWS Lambda for cost-sensitive, low-volume deployments

PostgreSQL + Redis vs NoSQL

Chosen: PostgreSQL + Redis Hybrid

• ✅ Pros: ACID compliance, mature tooling, flexible querying
• ❌ Cons: Operational overhead vs managed services
• Alternative: DynamoDB + ElastiCache for cloud-native architectures

AI Integration Decisions

OpenAI API vs Self-hosted Models

Chosen: OpenAI API with Self-hosted Fallback

• ✅ Pros: Latest models, reliability, comprehensive capabilities
• ❌ Cons: Vendor lock-in, cost at scale, data privacy concerns
• Alternative: Ollama for sensitive data or cost optimization

🤝 Contributing

Development Setup

1. Fork the repository
2. Create a feature branch: git checkout -b feature/amazing-feature
3. Install dependencies: npm install
4. Run tests: npm test
5. Submit a pull request

Code Standards

• TypeScript: Strict mode enabled
• ESLint: Enforced code style
• Prettier: Automatic formatting
• Jest: Comprehensive test coverage (>90%)
• Conventional Commits: Semantic commit messages

Pull Request Process

1. Ensure all tests pass
2. Update documentation as needed
3. Add tests for new functionality
4. Request review from maintainers
5. Address feedback and merge

📄 License

This project is licensed under the MIT License - see the file for details.

🙏 Acknowledgments

• Model Context Protocol for the foundational MCP specification
• OpenAI for GPT-4 and AI capabilities
• Anthropic for Claude and MCP development tools
• Open source contributors and the developer community

📞 Support

• Issues: GitHub Issues
• Discussions: GitHub Discussions
• Documentation: Wiki
• Email: team@your-org.com

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