Step 1
Install DV Quick Run
Install from the VS Code Marketplace, then open the DV Quick Run Hub from the command palette.
Command: DV Quick Run: Open Hub
Documentation
Get value quickly. Investigate calmly.
Practical starting points for Dataverse practitioners evaluating DVQR: install, run a focused query, inspect operational evidence, compare environments, reconstruct timeline evidence, generate Mini RCA briefings, and export investigation handoff reports.
Quickstart
First useful investigation in minutes.
Step 2
Connect to Dataverse
Use your existing Dataverse environment connection context. DVQR is designed to work where Dataverse practitioners already investigate, explain, and verify operational evidence.
Keep environment context visible before running operational workflows.
Step 3
Run a focused query
Start with the smallest OData or FetchXML query that reproduces the operational question. DVQR keeps raw query and JSON evidence available.
Useful first surfaces: Result Viewer, Copy Query, Copy JSON.
Step 4
Continue into operational evidence
Use Result Viewer actions, Query Understanding, Cross Diff Explain, Access Context, Operational Profiles, or comparison workflows to continue investigation without treating signals as certainty.
DVQR observes operational drift. DVQR does not fix operational drift.
Investigation Playbooks
Workflow-oriented guides for operational Dataverse questions.
Explain query intent
Use Query Understanding when a query needs operational interpretation rather than clause-by-clause syntax notes.
Open an OData query
Run Explain Query
Review Investigation Summary, Stage, Profile, and Confidence
Use Investigation Pattern and Things Worth Verifying before relying on the result as evidence
Explain comparison evidence
Use Cross Diff Explain before detailed evidence review to understand what changed, why it matters, how confident DVQR is, and where to investigate next.
Run Cross-Environment Diff
Open Reports → Cross Diff Explain
Review Investigation Summary and Key Operational Changes
Use provider evidence, audit evidence, and raw comparison data as the source of truth
Reconstruct operational timeline
Use Timeline Reconstruction with 3+ compatible snapshots from the same entity and environment to understand when drift was first observed.
Select compatible snapshots
Review the timeline graph
Inspect first-observed findings
Export Timeline Summary or Handoff reports, or open Timeline Understanding as an additional Markdown briefing
Generate Mini RCA briefing
Use Mini RCA (Experimental) after Timeline Understanding to turn timeline evidence into a cleaner investigation report with summary, story, deterministic reasoning, compact evidence, recommended next steps, and appendix-backed evidence references.
Run Timeline Reconstruction
Generate Timeline Understanding
Generate Mini RCA (Experimental)
Review the HTML or Markdown briefing as advisory and evidence-backed, not root-cause certainty
Compare environments before escalation
Use Cross-Environment Diff to compare operational participation, runtime behaviour, workflows, identity participation, relationship metadata, column metadata, choice metadata, entity configuration, environment variable current values, and reportable drift evidence.
Start with the same operational subject
Open Cross Diff Explain, then review top drift signals
Inspect grouped evidence
Export findings, handoff reports, DVAF, DVIM, DVCE, or DVEVM artifacts where eligible
Generate reconstruction handoff
Use eligible Metadata Attribute, Identity Participation, Choice Metadata, and Environment Variable Drift findings to export DVAF, DVIM, DVCE, or DVEVM reconstruction artifacts while keeping remediation and preview/apply outside DVQR.
Identify source-side drift
Export a DVAF, DVIM, DVCE, or DVEVM artifact
Preserve artifact references in reports
Review and apply in the companion utility
Investigate runtime behaviour
Use plugin step, workflow, solution participation, and runtime drift surfaces to understand observed behavioural differences without claiming root cause.
Review state, stage, mode, rank, and ownership signals
Inspect provider-owned evidence
Treat signals as verification prompts
Document unresolved review items
Understand access participation
Use Access Context to inspect bounded user, application user, team, role, and business-unit participation without simulating effective access.
Check direct and inherited participation
Review operational significance
Search local evidence
Export participation evidence when needed
Create investigation handoff
Use report exports to produce portable evidence summaries that help humans verify next steps outside DVQR.
Summarise observed drift
Preserve evidence references
Avoid remediation certainty
Hand off for external validation
Operational Principle
DVQR helps teams verify, not blindly fix.
DVQR surfaces operational evidence, comparison signals, and investigation continuity. It does not create deployment authority, remediation certainty, or autonomous root-cause claims. Humans remain responsible for validating operational decisions.
FAQ
Practical answers for buyers, evaluators, and Dataverse practitioners.
Does DVQR upload Dataverse investigation data?
No. DVQR is designed as a local-first operational investigation tool. Operational investigation data, Dataverse records, snapshots, exports, and comparison evidence are not uploaded during entitlement validation.
What is Mini RCA?
Mini RCA (Experimental) is an evidence-backed operational explanation layer built on Timeline Understanding. In v0.14.6 it opens with Executive Summary, Investigation Story, Why DVQR Thinks This, Evidence, and Recommended Next Steps, with Understanding Bundle details available in the Appendix. It does not claim root-cause certainty.
Does DVQR use AI to guess root cause?
No. DVQR focuses on evidence-backed operational investigation. It can help structure review and handoff, but it does not claim autonomous diagnosis, root-cause certainty, or remediation authority.
Does DVQR modify Dataverse?
DVQR is preview-first and evidence-first. Execution workflows are governed and explicit. Comparison and investigation surfaces are observational unless the user intentionally performs a supported execution action.
What is Free vs Pro?
Free keeps foundational operational understanding accessible. Pro accelerates advanced comparison, replay, timeline reconstruction, Mini RCA (Experimental), audit enrichment, runtime drift, identity participation drift, metadata drift coverage, DVAF/DVIM/DVCE/DVEVM reconstruction artifact exports, and portable investigation reports. Online Pro plans include a 14-day free trial.
What is Pathfinder?
Pathfinder Founder is the limited founder pricing tier for the first 200 customers. It includes the same Pro capabilities, a 14-day free trial, and $19/month founder pricing for as long as the subscription remains active.
What is Offline Pro?
Offline Pro is annual-only, manually issued licensing for regulated, locked-down, and air-gapped environments. Commercial transactions still flow through DV ForgeLab and Lemon Squeezy.
Community
Built with operational feedback.
Feedback, operational scenarios, feature ideas, and roadmap discussion happen through GitHub. DVQR evolves through rapid iteration, dogfooding, and Dataverse practitioner feedback.