dora-metrics

DORA Metrics

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

Instructions

All DORA metrics are served by a single resource type: sei_dora_metric. Pass the metric parameter to select the variant:

• deployment_frequency
• deployment_frequency_drilldown
• lead_time
• change_failure_rate
• change_failure_rate_drilldown
• mttr

Required inputs on every DORA call: team_ref_id, date_start, date_end, granularity (DAILY | WEEKLY | MONTHLY).

Step 1: Get a DORA Metric

Deployment Frequency:

Call MCP tool: harness_get
Parameters:
  resource_type: "sei_dora_metric"
  metric: "deployment_frequency"
  team_ref_id: "<team_id>"
  date_start: "2026-03-01"
  date_end: "2026-04-01"
  granularity: "WEEKLY"

Lead Time for Changes:

Call MCP tool: harness_get
Parameters:
  resource_type: "sei_dora_metric"
  metric: "lead_time"
  team_ref_id: "<team_id>"
  date_start: "2026-03-01"
  date_end: "2026-04-01"
  granularity: "WEEKLY"

Change Failure Rate:

Call MCP tool: harness_get
Parameters:
  resource_type: "sei_dora_metric"
  metric: "change_failure_rate"
  team_ref_id: "<team_id>"
  date_start: "2026-03-01"
  date_end: "2026-04-01"
  granularity: "WEEKLY"

Mean Time to Recovery:

Call MCP tool: harness_get
Parameters:
  resource_type: "sei_dora_metric"
  metric: "mttr"
  team_ref_id: "<team_id>"
  date_start: "2026-03-01"
  date_end: "2026-04-01"
  granularity: "WEEKLY"

Step 2: Get Drilldown Data

Per-deployment detail for frequency:

Call MCP tool: harness_get
Parameters:
  resource_type: "sei_dora_metric"
  metric: "deployment_frequency_drilldown"
  team_ref_id: "<team_id>"
  date_start: "2026-03-01"
  date_end: "2026-04-01"
  granularity: "DAILY"

Per-failure detail for CFR:

Call MCP tool: harness_get
Parameters:
  resource_type: "sei_dora_metric"
  metric: "change_failure_rate_drilldown"
  team_ref_id: "<team_id>"
  date_start: "2026-03-01"
  date_end: "2026-04-01"
  granularity: "DAILY"

Step 3: Get Team Data

List teams:

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

Get team details (integrations, developers, integration filters):

Call MCP tool: harness_list
Parameters:
  resource_type: "sei_team_detail"
  team_ref_id: "<team_id>"
  aspect: "developers"   # or "integrations" | "integration_filters"

Step 4: AI Metrics (Optional)

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

Related: sei_ai_impact, sei_ai_usage, sei_ai_raw_metric.

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_dora_metric	get (+ metric param)	All 6 DORA variants: deployment_frequency, deployment_frequency_drilldown, lead_time, change_failure_rate, change_failure_rate_drilldown, mttr
sei_team	list, get	Team definitions
sei_team_detail	list (+ aspect param: developers / integrations / integration_filters)	Per-team sub-resources
sei_metric	list, get	Generic metrics
sei_productivity_metric	get	Productivity metrics
sei_org_tree	list, get	Organization structure
sei_org_tree_detail	list, get	Org tree detail
sei_business_alignment	get	Business alignment
sei_ai_adoption	get	AI adoption metrics
sei_ai_impact	get	AI impact metrics
sei_ai_usage	get	AI usage metrics
sei_ai_raw_metric	get	Raw AI metrics

Examples

• "How are we doing on DORA metrics?" - Call sei_dora_metric four times with each primary metric
• "Compare DORA across teams" - List sei_team, then call sei_dora_metric per team_ref_id
• "What's our deployment frequency trend?" - Get sei_dora_metric with metric: deployment_frequency, then drilldown
• "Show AI adoption metrics" - Get sei_ai_adoption and related AI resources

Performance Notes

• Always pass team_ref_id, date_start, date_end, granularity — these are required.
• 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.

Troubleshooting

No Metric Data

• Verify SEI integrations are configured (Git, CI/CD, issue tracking)
• Confirm team_ref_id belongs to an active SEI team (harness_list resource_type: sei_team)
• Check the date range covers data the integrations have ingested
• Allow time for data collection and calculation after new integrations are added

Metrics Seem Incorrect

• Verify deployment detection rules in SEI settings
• Check failure classification criteria
• Review team member mappings via sei_team_detail aspect: developers