dora-metrics

name	dora-metrics
description	Generate DORA metrics and engineering performance reports using Harness SEI via MCP. Track deployment frequency, lead time, change failure rate, and MTTR. Use when user says "DORA metrics", "deployment frequency", "lead time", "engineering metrics", or asks about team performance.
metadata	{"author":"Harness","version":"2.0.0","mcp-server":"harness-mcp-v2"}
license	Apache-2.0
compatibility	Requires Harness MCP v2 server (harness-mcp-v2)

DORA Metrics

Generate DORA metrics reports using Harness Software Engineering Insights (SEI) via MCP.

Instructions

Step 1: Get DORA Metrics

Deployment Frequency:

Call MCP tool: harness_get
Parameters:
  resource_type: "sei_deployment_frequency"

Lead Time for Changes:

Call MCP tool: harness_get
Parameters:
  resource_type: "sei_lead_time"

Change Failure Rate:

Call MCP tool: harness_get
Parameters:
  resource_type: "sei_change_failure_rate"

Mean Time to Recovery:

Call MCP tool: harness_get
Parameters:
  resource_type: "sei_mttr"

Step 2: Get Drilldown Data

For detailed deployment frequency:

Call MCP tool: harness_get
Parameters:
  resource_type: "sei_deployment_frequency_drilldown"

For detailed change failure rate:

Call MCP tool: harness_get
Parameters:
  resource_type: "sei_change_failure_rate_drilldown"

Step 3: Get Team Data

Call MCP tool: harness_list
Parameters:
  resource_type: "sei_team"

Call MCP tool: harness_list
Parameters:
  resource_type: "sei_team_developer"

Step 4: AI Metrics (Optional)

Call MCP tool: harness_get
Parameters:
  resource_type: "sei_ai_adoption"

Also: sei_ai_adoption_breakdown, sei_ai_impact, sei_ai_usage, sei_ai_usage_breakdown

DORA Benchmarks

Metric	Elite	High	Medium	Low
Deployment Frequency	Multiple/day	Weekly-Monthly	Monthly-6mo	6mo+
Lead Time	< 1 hour	1 day-1 week	1-6 months	6mo+
Change Failure Rate	< 5%	5-10%	10-15%	> 15%
MTTR	< 1 hour	< 1 day	1 day-1 week	1 week+

Report Format

## DORA Metrics Report

**Period:** <date range>
**Team:** <team or org>

### Performance Summary

| Metric | Value | Rating | Trend |
|--------|-------|--------|-------|
| Deployment Frequency | X/week | High | Improving |
| Lead Time | X hours | Elite | Stable |
| Change Failure Rate | X% | Medium | Needs attention |
| MTTR | X hours | High | Improving |

### Overall Rating: <Elite/High/Medium/Low>

### Recommendations
1. CFR at X% - invest in test automation and code review
2. Lead time trending up - look at PR review bottlenecks
3. Consider feature flags to decouple deploy from release

SEI Resource Types

Resource Type	Operations	Description
sei_deployment_frequency	get	Deploy frequency metric
sei_deployment_frequency_drilldown	get	Detailed frequency data
sei_lead_time	get	Lead time for changes
sei_change_failure_rate	get	Change failure rate
sei_change_failure_rate_drilldown	get	Detailed CFR data
sei_mttr	get	Mean time to recovery
sei_team	list, get	Team definitions
sei_team_developer	list	Team members
sei_metric	list, get	Generic metrics
sei_org_tree	get	Organization structure
sei_business_alignment	get	Business alignment
sei_ai_adoption	get	AI adoption metrics

Examples

• "How are we doing on DORA metrics?" - Get all 4 metrics, compare to benchmarks
• "Compare DORA across teams" - List teams, get metrics per team
• "What's our deployment frequency trend?" - Get sei_deployment_frequency with drilldown
• "Show AI adoption metrics" - Get sei_ai_adoption and breakdown

Performance Notes

• Gather metrics across the full requested time range before generating the report. Partial data skews results.
• Compare metrics across multiple time periods to identify trends, not just snapshots.
• Quality of analysis and actionable recommendations is more important than speed.

Troubleshooting

No Metric Data

• Verify SEI integrations are configured (Git, CI/CD, issue tracking)
• Check team mappings exist
• Allow time for data collection and calculation

Metrics Seem Incorrect

• Verify deployment detection rules in SEI settings
• Check failure classification criteria
• Review team member mappings