Observability Dashboard Analyzer

Production-ready MCP Server for intelligent Loki log analysis and Grafana dashboard generation based on industry-standard monitoring methodologies (RED Method).

🎯 Key Features

- Smart Analysis: Analyzes Loki logs to recommend optimal dashboard configurations
- RED Method Implementation: Industry-standard Rate, Errors, Duration monitoring dashboards
- Service Discovery: Automatic detection of services from log data
- Query Optimization: Performance optimization suggestions for LogQL queries
- Production Ready: Handles authentication, errors, and enterprise deployment scenarios

🚀 Quick Start

$3

``bash
npm install -g observability-dashboard-analyzer
`

$3

The tool uses multi-tier configuration (environment variables > config file > defaults):

1. Environment Variables (highest priority):
`bash
export LOKI_URL=http://localhost:3100
export LOKI_USERNAME=your-username
export LOKI_PASSWORD=your-password
`

2. Config File (medium priority):
Edit ~/.observability-analyzer/config.json:
`json
{
"loki": {
"url": "http://localhost:3100",
"auth": {
"type": "basic",
"username": "your-username",
"password": "your-password"
}
}
}
`

$3

Add to your Claude Desktop MCP configuration:

`json
{
"mcpServers": {
"observability-analyzer": {
"command": "npx",
"args": ["observability-dashboard-analyzer"]
}
}
}
`

🔧 MCP Tools

$3

#### analyze_loki_stack
Analyzes your Loki setup and discovers services with log structure analysis.

`typescript
// Example usage in Claude
"Analyze my Loki logs for the last 24 hours"
`

Returns:
- Service discovery from log data
- Log structure quality assessment
- Available labels and error patterns
- Dashboard recommendations based on data structure

#### generate_loki_dashboard
Creates production-ready Loki monitoring dashboard with service-specific panels.

`typescript
"Generate a dashboard for my payment-api and user-service"
`

Features:
- Log volume monitoring by service
- Error rate tracking with thresholds
- Log level distribution analysis
- Service health overview
- Error pattern detection

#### validate_loki_queries
Tests LogQL queries against real Loki API to validate query performance.

`typescript
"Validate these LogQL queries for performance"
`

Features:
- Query syntax validation
- Performance optimization suggestions
- Success rate analysis
- Query execution testing

#### export_loki_dashboard
Exports Loki dashboard to Grafana-compatible JSON file.

`typescript
"Export dashboard for my services to a JSON file"
`

Features:
- Grafana-compatible JSON export
- Service-specific configurations
- Optimized LogQL queries
- Production-ready dashboard structure




🏗️ Architecture

$3

The tool implements monitoring methodologies based on industry research:

- RED Method (Request rate, Error rate, Duration) - Universal microservices standard
- Service Discovery - Automatic detection from log labels and content
- Log Structure Analysis - Quality assessment for dashboard feasibility

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Dashboards are recommended based on log data analysis:

- Service Discovery: Automatic detection from service labels and JSON content
- Log Structure Quality: Assessment of structured logs and error patterns
- Volume Analysis: Log throughput and service activity measurement

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The tool provides performance optimization suggestions:

- LogQL Optimization: Avoid regex wildcards, use exact string matching first
- Label Filtering: Use specific label selectors for better performance
- Query Patterns: Efficient time-based and service-based queries

🔒 Security & Authentication

Supports authentication methods:

- Basic Auth: Username/password authentication (most common)
- No Auth: For local development setups

📊 Dashboard Features

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1. Log Volume Monitoring
- Service-based log volume tracking
- Request rate analysis from log data
- Time-based volume patterns

2. Error Rate Tracking
- Error detection from log patterns
- Industry-standard thresholds (1% yellow, 5% red)
- Error pattern analysis

3. Service Health Overview
- Multi-service comparison
- Log level distribution
- Service activity monitoring

🧪 Testing

Run the comprehensive test suite:

`bash


Run all tests

npm test

Run with coverage

npm run test:coverage

Run specific test suites

npm test -- red-method.test.ts
`

Test Coverage:
- Unit tests for dashboard generators
- Integration tests with mocked Loki APIs
- Query validation and optimization testing
- Configuration and authentication testing

🚀 Development

$3

`
src/
├── index.ts # MCP server entry point
├── config/
│ ├── ConfigManager.ts # Multi-tier configuration
│ └── AuthHandler.ts # Authentication handling
├── analyzers/
│ └── LokiAnalyzer.ts # LogQL query generation & analysis
├── dashboards/
│ ├── REDMethodGenerator.ts # RED method dashboards
│ └── GrafanaExporter.ts # Dashboard export utilities
├── types/
│ ├── loki-api.ts # Loki API types
│ ├── grafana-config.ts # Grafana dashboard types
│ ├── monitoring-methods.ts # Monitoring methodology types
│ └── config.ts # Configuration types
└── __tests__/ # Test suite
`

$3

`bash
npm run build # TypeScript compilation
npm run lint # ESLint checking
npm run typecheck # TypeScript type checking
`

📈 Success Metrics

- Functional: Generates working dashboards for common Loki setups
- Performance: Analyzes Loki logs in <30 seconds
- Usability: npm install -g → working in Claude within 5 minutes
- Professional: 90%+ test coverage + comprehensive TypeScript types

🤝 Contributing

1. Fork the repository
2. Create a feature branch: git checkout -b feature/amazing-feature
3. Run tests: npm test
4. Commit changes: git commit -m 'Add amazing feature'
5. Push to branch: git push origin feature/amazing-feature`
6. Open a Pull Request

📄 License

MIT License - see LICENSE file for details.

🔗 Links

- RED Method Documentation
- Loki Documentation
- LogQL Documentation

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Built for production observability teams who need immediate value from their Loki setup. 🚀