MCP tool: pm_ice_score

ICE Scoring

Multiply Impact, Confidence, and Ease to compare ideas using a shared rating scale.

When to use this

You're a solo PM or a small team doing a quick sort on 5-8 ideas. Reach is roughly the same across the candidates (a dashboard feature for users who already use the dashboard). You want a ranking now, not a research project.

Limits

When Reach differs substantially, consider RICE or include Reach in another scoring method. ICE also needs clear rating definitions and supporting evidence; adding more items does not itself invalidate the scale.

Inputs

  • Impact: How much the feature moves your goal. 1-10 scale.
  • Confidence: How sure you are about Impact and Ease. 1-10 scale.
  • Ease: How easy it is to build and ship. 1-10, where 10 is trivial and 1 is a heavy lift.

The math

Score = Impact x Confidence x Ease

All three on the same scale, all multiplied. The maximum is 1,000 and the minimum is 1. Higher score, higher priority.

A worked example

Say you're a solo PM running a quarterly review of 6 ideas. You score each axis 1-10.

IdeaImpactConfidenceEaseScore
Add a "Recently viewed" list689432
Redesign settings page475140
Auto-save drafts897504
Slack integration744112
Keyboard shortcuts588320
Multi-language support962108

Auto-save drafts ranks first (504), followed by Recently viewed (432). Low Ease reduces the multi-language score to 108. The Slack integration score reflects lower Confidence and Ease.

Review the evidence behind low-confidence ideas before treating the ranking as a commitment.

In PM Toolkit

PM Toolkit multiplies the three 1–10 ratings, ranks the items, and provides score-based labels. These labels describe the entered scores, not a validated business priority. The MCP tool uses the same multiplication.

Common mistakes

  • Forcing a spread. Similar scores may reflect similar ideas; clarify the scale without changing ratings just to create separation.
  • Raising Confidence without evidence. Document what supports each rating.
  • Using ICE when Reach varies a lot. A feature for 50 users and a feature for 50,000 users shouldn't rank by the same three numbers.

Try it