DV Quick Run

DV Quick Run v1.0.0 is here. 🎉

Understand Dataverse applications. Investigate operational behaviour. Stay grounded in evidence.

DV Quick Run is a metadata-aware Dataverse investigation workbench for VS Code with an extension-owned Local MCP server. It brings querying, schema intelligence, relationship discovery, reusable Business Paths, operational evidence, professional investigation and bounded Mini RCA into one local-first workflow.

Instead of jumping between metadata browsers, query tools, logs, spreadsheets and disconnected AI conversations, DV Quick Run lets you move through the investigation as one explainable journey:

understand
   ↓
query
   ↓
discover relationships
   ↓
verify real business paths
   ↓
collect evidence
   ↓
investigate
   ↓
assess readiness & gaps
   ↓
Mini RCA
   ↓
handoff / preserve

Metadata-backed · Evidence-aware · Preview-first · Local-first · Human-controlled

Website · Pricing · GitHub Discussions

🎉 DV Quick Run v1.0.0

After evolving from a Dataverse query utility into a complete investigation workbench, DV Quick Run has reached v1.0.0.

v1 establishes the stable product contract for DVQR:

• understand unfamiliar Dataverse applications from metadata
• query and explain OData, FetchXML and $batch
• discover how tables actually relate
• test multi-hop routes against real records
• preserve verified routes as Managed Business Paths
• investigate operational behaviour with canonical evidence
• compare environments and reconstruct timelines
• inspect Operational Profiles, Access Context and execution evidence
• run persisted Professional Investigations
• assess evidence readiness and gaps
• generate bounded, evidence-backed Mini RCA
• talk to Dataverse through an extension-owned Local MCP server
• keep execution authority, evidence meaning and final judgement outside the model

This is not simply a version-number milestone. v1 is the point where DVQR's query, understanding, traversal and investigation capabilities form one coherent product.

✨ What Ships in v1.0.0

🧠 Metadata-Aware Dataverse Understanding

DVQR starts from Dataverse metadata rather than guessing schema semantics.

• deterministic table, column and relationship discovery
• navigation-property and polymorphic-lookup understanding
• business architecture and operational workflow intelligence
• capability and Custom API discovery
• confidence, provenance and uncertainty preserved

🔎 Query, Explain & Refine

• OData and FetchXML execution
• $batch workflows
• natural-language OData through Local MCP
• Query Doctor and Query Explain
• metadata-aware suggestions
• Query-by-Canvas refinement
• lookup-aware $expand guidance
• Result Viewer
• preview-first Smart PATCH in the editor

🧭 Relationship Intelligence & Guided Traversal

• discover and rank relationship paths
• resolve exact navigation properties
• Relationship Graph
• carry landed records from hop to hop
• distinguish metadata-valid from data-viable routes
• explain the exact frontier where traversal becomes empty

🛤️ Test Business Traverse & Managed Business Paths

One of v1's defining capabilities is turning discovered traversal knowledge into reusable workspace knowledge.

discover candidate paths
        ↓
test against real records
        ↓
observe reached hops
        ↓
rank viable routes
        ↓
explicit Save
        ↓
Reverify metadata
        ↓
Runtime Verify exact saved route
        ↓
reuse / govern

DVQR keeps metadata-valid, runtime-observed, ObservedNonEmpty, ObservedEmpty, NotReached, saved, Runtime Verified and BusinessPreferred meaningfully separate.

If an exact route reaches an empty frontier, that route STOPs. DVQR does not silently substitute another relationship or turn the empty frontier into a table-wide query. Broader exploration requires an explicit new Business Path scope.

📊 Operational Profiles & DVQR Score

Operational Profiles provide a bounded, evidence-backed view of an entity's operational footprint, including relationship complexity, plugin orchestration, async participation, Power Automate/workflow involvement and managed-state context.

The DVQR Score is a calibrated investigation aid—not a risk, health or root-cause score.

👤 Access Context

Investigate bounded identity participation across users, application identities, teams, roles and business units.

Access Context does not simulate RBAC or claim effective record access.

📸 Evidence Workspace & Snapshot Library

Preserve snapshots, comparisons, Business Paths, reports, investigation artifacts and handoff material locally under .dvforgelab.

🔀 Cross-Environment Diff

Compare compatible evidence across environments, review grouped operational drift, preserve verification state and export investigation-ready reports.

🕒 Timeline Reconstruction & Audit Evidence

Reconstruct snapshot-bounded change intervals with Timeline Graph, trust, findings, handoff and optional Audit Evidence Enrichment.

First-observed drift is not presented as an exact historical change time or proof of causality.

⚙️ Custom API Intelligence & Governed Execution

• Functions vs Actions
• bound vs unbound operations
• metadata-backed definitions
• architecture recommendations
• execution readiness
• preview
• explicit confirmation
• guarded eligible execution
• execution interpretation

discover → explain → preview → confirm → execute → inspect evidence

🧪 Professional Investigation

Pro turns individual evidence tools into a persisted investigation workflow.

Start Investigation
      ↓
confirm / edit intent
      ↓
bounded Continue
      ↓
acquire explicit evidence
      ↓
inspect trace & evidence
      ↓
Readiness / Evidence Gaps
      ↓
Mini RCA
      ↓
verify / hand off

Professional Investigation coordinates. Providers acquire evidence. Resume restores state; it does not silently reacquire evidence.

🎯 Investigation Readiness & Evidence Gaps

DVQR keeps acquired, observed-zero, unavailable, unsupported, access-limited, failed, stale/historical and NotReached states distinct.

Evidence Gaps remain visible instead of being filled with confident prose.

🔬 Evidence Correlation

DVQR can correlate evidence across providers while preserving a permanent rule:

Participation is not causality.

🧩 Mini RCA

Mini RCA synthesizes already-acquired evidence into bounded observations, hypotheses, limitations, verification recommendations and handoff material.

Mini RCA is zero-acquisition. Regenerate Mini RCA is also zero-acquisition.

DVQR stops at explanation and handoff rather than silently progressing into remediation or deployment.

🤖 Talk to Dataverse with Local MCP

DV Quick Run includes an extension-owned Local MCP server.

v1 catalogue

• 32 Free MCP tools
• 32 Pro MCP tools
• 64 tools total

Prompt Library

• 69 Free prompts
• 25 Pro prompts
• 94 guided prompts total

The model can propose work. DVQR decides what may execute and what the resulting evidence means.

🚀 Three Ways to Use DV Quick Run

DV Quick Run: Open Prompt Library
DV Quick Run: Open Hub

🆓 Free vs Pro

DV Quick Run follows an open-core model.

Free is the investigation foundation. Pro accelerates and deepens professional investigation.

Free truth = Pro truth
Free safety = Pro safety

A 14-day Pro Trial is available for evaluating the complete investigation workflow.

🛡️ Built for Evidence, Not AI Guesswork

DVQR application code—not prompt text or model output—owns the active environment, registered capability, entitlement, canonical identifiers, execution bounds, workspace containment, confirmation requirements and evidence-state semantics.

Security hardening includes permanent deterministic adversarial regression across hostile content, capability spoofing, entitlement bypass, environment confusion, replay, unsafe tool chaining, traversal/resource abuse, path escape and diagnostic exfiltration scenarios.

This is engineering security qualification—not a security certification.

🛑 Guardrails That Matter

DVQR does not:

• silently persist Business Paths
• treat runtime success as BusinessPreferred
• substitute a different relationship during exact-route verification
• continue past an empty Business Path frontier
• treat NotReached as zero
• convert metadata validity into runtime truth
• convert participation into causality
• simulate effective RBAC
• treat first-observed drift as an exact historical timestamp
• treat audit rows as proof of causality
• allow Mini RCA to acquire evidence
• treat generated recommendations as execution authority
• automatically remediate or deploy from an investigation

Humans retain operational authority.

🌟 Why v1 Matters

DV Quick Run started with a simple idea: make Dataverse investigation faster without making it less trustworthy.

v1 brings the pieces together:

query workbench
   + metadata intelligence
   + relationship understanding
   + runtime traversal
   + reusable Business Paths
   + evidence workspace
   + professional investigation
   + bounded AI assistance
   = DV Quick Run v1

The goal is not to make Dataverse investigation magical.

The goal is to make it faster, more explainable, more repeatable and easier to hand off—without losing sight of what was actually observed.

5-Minute Quick Start

A. Run DV Quick Run normally

1. Install DV Quick Run in VS Code.

2. Configure and select a Dataverse environment.

3. Run a query such as:

   contacts?$top=10
   

4. Open DV Quick Run: Open Hub whenever you need orientation.

B. Enable Local MCP

1. Select the Dataverse environment to expose to the local server.

2. Run:

   DV Quick Run: Enable Local MCP Server
   

3. Sign into the environment tenant with Azure CLI. For a tenant without an Azure subscription:

   az login --tenant <tenant-id> --allow-no-subscriptions
   

4. Open GitHub Copilot Chat and make sure the DV Quick Run MCP tools are enabled.

5. Open DV Quick Run: Open Prompt Library, or ask directly:

   Using DV Quick Run, show me what I can investigate in this Dataverse environment and recommend where to start.
   

DV Quick Run remembers MCP enablement per workspace. VS Code starts the extension-owned local stdio server on demand.

Local MCP

DV Quick Run owns its local MCP lifecycle inside the extension. The Hub shows the selected environment, mode, tool count, authentication guidance and traffic-light health state.

The foundational MCP surface is deliberately bounded. It supports metadata understanding, relationship intelligence, natural-language querying and evidence acquisition without turning the assistant into an unrestricted Dataverse administrator.

Typical prompts:

Using DV Quick Run, find tables related to customers.

Using DV Quick Run, explain this OData query:
accounts?$select=name,revenue&$filter=statecode eq 0&$orderby=name asc&$top=10

Using DV Quick Run, starting only from Contact metadata, discover the business capabilities and explain where operational work is coordinated and performed.

Using DV Quick Run, find and runtime-verify a relationship path from account to task.

Business Architecture Understanding

Operational Workflow Intelligence separates structural evidence into:

• Core Domain — principal business, service, case, plan or request concepts
• Coordination — journey, process, routing and orchestration records
• Execution — tasks, activities and downstream work items
• Governance — eligibility, consent, approval, safety and control records
• Platform — plugins, flows, asynchronous jobs and integration participation

Strong structural evidence does not prove that a particular record participated. Runtime evidence remains investigation-scoped and does not overwrite metadata confidence.

Managed Business Paths

Managed Business Paths turn verified traversal knowledge into a reusable workspace asset.

metadata-valid path
        ↓
runtime verification
        ↓
explicit save
        ↓
Preferred workspace path
        ↓
record-scoped exact-hop Guided Traversal

A Preferred path is top visible, not exclusive. Metadata-derived alternatives remain available. Preference records useful team knowledge; it does not replace Dataverse metadata truth or prove universal business authority.

Saved paths live under:

.dvforgelab/dvqr/business-paths

The runtime frontier is intentionally bounded:

landed record IDs → exact next relationship → landed record IDs

If a hop returns no rows, traversal stops. DVQR does not convert an empty frontier into a table-wide query.

Result Viewer & Guided Traversal

The Result Viewer is the main interactive surface for exploring query results.

start simple → run → explore → refine → investigate → verify

Use it to:

• view records as table or JSON
• search and inspect results
• refine queries
• launch record investigation
• open relationship navigation
• continue Guided Traversal
• inspect operational context
• preview compatible bound Actions
• export evidence and supported handoff artifacts

Guided Traversal carries actual landed records from hop to hop. Managed Business Paths can reuse the exact saved route from a supplied source record.

Operational Profiles & DVQR Score

Operational Profiles describe the operational footprint of a Dataverse entity before deeper troubleshooting.

Profiles can surface:

• plugin orchestration density
• relationship complexity
• metadata footprint
• async participation
• Power Automate involvement
• workflow participation
• managed-state context

Profiles are entity-scoped, user-triggered, evidence-backed, bounded and advisory-only. The DVQR Score is a calibrated investigation aid, not a risk or root-cause score.

Access Context

Access Context investigates bounded identity participation for:

• users and application identities
• teams
• roles
• business units

It can show business-unit context, direct and inherited participation, team membership and supporting evidence.

It does not simulate RBAC, calculate effective record access, generate privilege matrices or infer security risk.

Managed Investigation

Pro managed investigations preserve professional investigation continuity rather than treating each prompt as an isolated answer.

prepare & confirm
      ↓
acquire bounded evidence
      ↓
assess readiness
      ↓
generate bounded Mini RCA
      ↓
verify / hand off

The evidence model keeps supported, weakened and unresolved hypotheses distinct and preserves gaps rather than filling them with speculation.

Evidence Workspace, Comparison & Timeline

DV Quick Run uses a local DV ForgeLab Evidence Workspace for investigation artifacts and reconstruction handoffs.

.dvforgelab
└─ dvqr
   ├─ business-paths
   ├─ comparisons
   ├─ reports
   └─ snapshots

Snapshot Library

Snapshot Library coordinates saved operational snapshots and comparison/timeline selection.

Cross-Environment Diff

Compare compatible snapshots across environments and review grouped operational drift with evidence references, verification state and handoff context.

Timeline Reconstruction

Select 3+ compatible snapshots from the same environment and entity to reconstruct snapshot-bounded intervals and first-observed drift.

DVQR can surface:

• Timeline Graph
• provider and significance distributions
• Timeline Trust
• Timeline Findings Summary
• Timeline Investigation Handoff
• optional Audit Evidence Enrichment

Timeline findings describe when drift was first observed between captures. They do not claim an exact change time, root cause, human responsibility or remediation status.

Capability Explorer & Governed Execution

Capability Explorer discovers and explains supported Dataverse operations and Custom APIs.

It distinguishes:

• Functions vs Actions
• bound vs unbound operations
• public vs private visibility
• preview-ready vs inspect-only capability
• execution eligibility
• operational-impact cautions

Supported execution follows:

preview → explicit confirmation → execution → inspect result → investigate evidence

Custom API metadata is discovery truth; OData metadata is execution-exposure truth; bound route metadata is execution-route truth; the active environment remains the execution authority boundary.

AI-related operation execution is blocked by default unless explicitly allowed by policy, and generated output still requires human review.

DV ForgeLab Ecosystem

DV Quick Run investigates. Other DV ForgeLab utilities reconstruct or execute focused changes through explicit handoff artifacts.

Supported ecosystem handoffs include:

• DVBUR — focused bulk upsert artifacts
• DVAF — supported attribute reconstruction intent
• DVIM — identity management artifacts
• DVCE — choice artifacts
• DVEVM — environment variable artifacts

Investigation and reconstruction remain separate concerns.

Guardrails

DV Quick Run favours explicit, preview-first, user-controlled workflows.

Key boundaries include:

• bounded queries and explicit execution context
• no silent Managed Business Path persistence
• no broadening after an empty traversal frontier
• no causal claims from participation alone
• no effective-access claims from Access Context participation
• no cross-environment timeline reconstruction
• no treatment of first-observed drift as exact historical time
• no treatment of audit rows as proof of causality
• no treatment of reports or exports as approval/certification
• no treatment of reconstruction artifacts as automatic remediation
• explicit confirmation for supported execution-capable workflows

DV Quick Run does not treat generated or AI-assisted responses as operational truth. Human verification remains the authority boundary.

Pro Activation

DV Quick Run is available in Free and Pro editions.

Pricing: https://www.dvquickrun.com/pricing

Purchase: https://dvforgelab.lemonsqueezy.com

Activate Online Pro from the Command Palette:

DV Quick Run: Activate Pro License

Inspect entitlement:

DV Quick Run: License Status

Restricted or disconnected environments can use signed Offline Pro licensing:

DV Quick Run: Import Offline License

Who Is This For?

• Dataverse / Dynamics 365 developers
• Power Platform engineers
• integration and API developers
• support engineers investigating Dataverse behaviour
• consultants working across complex Dataverse environments
• teams onboarding into unfamiliar Dataverse solutions

Why DV Quick Run?

Because useful Dataverse investigation is more than running a query:

understand → query → verify → investigate → compare → explain → hand off

DV Quick Run reduces tool switching while keeping metadata truth, runtime evidence, user preference, execution authority and human judgement explicitly separated.

Community & Feedback

GitHub Discussions: https://github.com/yongjinsim-sudo/dv-quick-run/discussions

Official website: https://www.dvquickrun.com

Use Discussions to report bugs, suggest features, share workflow feedback, discuss investigation patterns and submit evidence edge cases.

Development

npm install
npm run compile

Press F5 to run the extension.

For direct developer MCP verification after compiling:

npm run mcp:start

For interactive inspection:

npm run mcp:inspect

Azure CLI authentication must already be available through az login.

License & Open-Core Model

DV Quick Run follows an open-core model.

The MIT-licensed core preserves foundational Dataverse understanding workflows. Proprietary Pro modules provide advanced investigation acceleration, persistence, comparison, timeline, reporting and governed execution capabilities.

/src/core
MIT open-core functionality

/src/pro
Private proprietary acceleration modules
Not included in the public repository or MIT grant

Foundational operational understanding remains accessible. Commercial acceleration funds continued development.