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":"2.1.0","mcp-server":"harness-mcp-v2"}
license	Apache-2.0
compatibility	Requires Harness MCP v2 server (harness-mcp-v2). All six DORA metric variants are served by a single consolidated `sei_dora_metric` resource — pick the variant via the `metric` parameter.

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