Checklist against known search UX patterns
Each capability is weighted by how strongly it affects a user's ability to find what they need — typo tolerance and empty-result recovery weigh heavily since failed searches directly cause drop-off.
Free MVP search UX tool
SearchUX AI
A structured self-assessment of your in-product search — checking query handling, suggestions, filtering, and empty-result recovery against known best practices, using only what you enter.
- Calculated instantly from your inputs
- No signup or data submission
- A focused next step for MVP planning
Your entries remain in this browser session and are not sent to MVPHub.
Your inputs
Describe your current search experience
List one search capability per line as Capability | present (yes/no). Cover the features relevant to your product's search.
Your calculated result
Your result
0/100
Planning score
Recommended next actions
How it works
Volume-aware scoring
The more searches your product handles monthly, the more a missing capability costs in aggregate failed searches — the score reflects that scaling.
Recovery-first recommendations
Gaps in empty-result handling and typo tolerance are surfaced first, since a search that returns nothing is the most damaging failure mode.
Frequently asked questions
Is this a real AI search analysis?
No — this is a structured self-assessment tool. It scores your own answers against a transparent, documented checklist; it does not call any AI model or inspect your product directly.
What matters most if I can only fix one thing?
Empty-result handling — showing suggestions, related items, or a way to broaden a failed search — tends to have the largest impact on recovering an otherwise lost session.
Do I need autocomplete for a small dataset?
Autocomplete matters far less below a few hundred searchable items — prioritize it once your dataset or search volume grows.
How is this different from FilterFlow?
SearchUX AI assesses the free-text search box experience itself; FilterFlow designs the structured facet/filter UI for browsing a dataset. Many products need both.
How we compare
| Capability | MVPHub | Guessing based on gut feel | A general-purpose AI assistant |
|---|---|---|---|
| Uses a documented, repeatable checklist | ✓ | × | — |
| Weighs gaps by your actual search volume | ✓ | × | × |
| Produces a prioritized fix list | ✓ | × | — |
| Instant, no signup required | ✓ | ✓ | ✓ |
MVPHub applies a consistent, transparent checklist to your own inputs. Gut-feel review varies person to person; a general AI assistant lacks a fixed, repeatable scoring method.