New Relic

Based on my analysis of New Relic as an observability platform, here's the assessment:

```json
{
  "service_type": "platform",
  "base_url": "https://newrelic.com",
  "auth_method": "api_key",
  "auth_config": {
    "api_key_location": "header",
    "header_name": "Api-Key",
    "additional_auth": {
      "license_key": "for data ingest",
      "user_key": "for user-specific operations"
    }
  },
  "endpoints": [
    {"path": "/v2/applications", "method": "GET", "purpose": "List applications"},
    {"path": "/v2/alerts_policies", "method": "GET", "purpose": "Manage alert policies"},
    {"path": "/v1/insights/query", "method": "GET", "purpose": "NRQL query execution"},
    {"path": "/graphql", "method": "POST", "purpose": "NerdGraph GraphQL API"},
    {"path": "/v2/servers", "method": "GET", "purpose": "Infrastructure monitoring"}
  ],
  "pricing_model": {
    "type": "freemium", 
    "details": {
      "free_tier": "100 GB/month data ingest, 1 full-access user",
      "paid_tiers": "Based on data ingest volume and user seats",
      "enterprise": "Custom pricing for large deployments"
    }
  },
  "rate_limits": {
    "api_calls": "3600 requests per hour per API key",
    "data_ingest": "Based on subscription tier"
  },
  "capabilities": [
    "Application Performance Monitoring (APM)",
    "Infrastructure monitoring and alerting", 
    "Real User Monitoring (Browser)",
    "Mobile application monitoring",
    "Synthetic monitoring and testing",
    "Log management and analysis",
    "Distributed tracing",
    "Custom dashboards and visualization",
    "NRQL query language for data analysis",
    "Alert management and notification workflows",
    "Kubernetes and container monitoring",
    "AWS/Azure/GCP cloud integrations",
    "Custom metrics and events via API",
    "Webhook integrations",
    "Slack/PagerDuty/ServiceNow integrations"
  ],
  "raw_analysis": "New Relic is a mature, enterprise-grade observability platform that serves as the central nervous system for modern applications. It targets DevOps teams, SREs, and engineering organizations who need comprehensive visibility into their application performance, infrastructure health, and user experience. The platform excels in its unified data model that correlates metrics, events, logs, and traces in a single interface. Key differentiators include their NRQL query language for flexible data exploration, extensive out-of-the-box integrations with popular frameworks and cloud services, and their agent-based architecture that provides deep visibility into application behavior. The freemium model makes it accessible for smaller teams while enterprise features scale to support large, complex environments. Their REST and GraphQL APIs enable programmatic access for automation and custom integrations, making it highly suitable for agent-driven workflows that need to query performance data, manage alerts, or integrate observability into CI/CD pipelines."
}
```

2/3 tests passed

{"multi_model": true, "models_used": ["openai", "claude_cli"], "model_scores": {"GPT-4o": {"overall": 83, "dimensions": {"token_efficiency": 8.0, "first_try_success": 7.0, "response_parseability": 9.0, "error_clarity": 7.5, "doc_quality": 8.5, "auth_simplicity": 7.0, "latency": 10.0, "consistency": 9.0}}, "Claude CLI": {"overall": 87, "dimensions": {"token_efficiency": 9.0, "first_try_success": 7.0, "response_parseability": 10.0, "error_clarity": 8.0, "doc_quality": 9.0, "auth_simplicity": 8.0, "latency": 10.0, "consistency": 9.0}}}, "averaged": true}