DV Quick Run

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

DV Quick Run is an extension-owned Local MCP server and metadata-aware Dataverse investigation workbench for VS Code. Use GitHub Copilot to understand unfamiliar business architecture and query Dataverse in natural language, or continue in the editor with OData, FetchXML, Result Viewer, Guided Traversal, Operational Profiles, Cross-Environment Diff, Timeline Reconstruction and Mini RCA.

Read-only MCP · Deterministic metadata · Evidence-backed conclusions · Human authority

What is DV Quick Run?

DV Quick Run gives Dataverse developers, consultants and support teams two connected ways to work:

Talk to Dataverse through Local MCP — discover business capabilities, identify operational anchors, explain workflow layers, search metadata, generate verified OData and run bounded read-only probes.
Build and investigate in the editor — write and execute queries, inspect results, follow relationships, capture evidence, compare environments, reconstruct timelines and generate bounded Mini RCA reports.

The product is organised around four outcomes:

Understand applications — translate structural metadata into a layered Business Capability Landscape.
Query and explain — use natural language or editor workflows to create, validate and understand OData and FetchXML.
Investigate runtime behaviour — move from records and queries into Execution Insights, Operational Profiles, Access Context and evidence-backed traversal.
Compare and reconstruct — use snapshots, Cross-Environment Diff, Timeline Reconstruction, Audit Evidence and Mini RCA without claiming unsupported causality.

DV Quick Run follows a simple investigation loop:

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

Custom API Intelligence — v0.15.7

DV Quick Run now provides a complete metadata-aware Custom API lifecycle through Local MCP:

Discover → Explain → Compare → Recommend → Architecture → Preview → Execute → Interpret

Discover and explain: inspect exact public Custom API metadata, operation and binding shape, inputs, outputs, and bounded usage guidance.
Compare and recommend: evaluate named APIs or a natural-language goal without substituting unsupported capabilities.
Solution Architecture: assemble semantically compatible APIs into ordered Recommended, Simpler, and Extended pipelines with confidence and evidence boundaries.
Preview-first execution: validate required inputs and review the exact method, route, body, expected outputs, side-effect posture, preview ID, and expiry.
Explicit confirmation: execution only continues after a later user reply of EXECUTE; preview sessions are short-lived, single-use, and atomically consumed.
Live execution: the initial MCP scope is deliberately narrow—public global generate-only Actions with supported scalar inputs.
Execution Intelligence: successful and failed calls receive an execution ID and can be interpreted later from stored runtime evidence without rerunning or contacting Dataverse.

Execution reports distinguish observed runtime evidence from inference. Administrative, private, bound, complex, mutation-like, and unsupported operations remain outside the initial MCP execution policy. Basic understanding and execution remain Free; deeper orchestration, environment-aware intelligence, and productivity acceleration remain natural Pro territory.

Local MCP — Business Architecture Understanding

DV Quick Run v0.15.6 provides an extension-owned local stdio MCP server for GitHub Copilot and compatible VS Code MCP clients. Enable it once per workspace with DV Quick Run: Enable Local MCP Server; VS Code then starts the server on demand and DV Quick Run remembers the workspace preference.

The Free MCP surface is strictly read-only. It can:

discover business capabilities and operational anchors;
produce a layered Business Capability Landscape;
explain why architectural conclusions were selected;
show confidence, evidence provenance and inline uncertainty;
discover metadata-verified relationship paths;
generate and explain OData;
execute bounded Dataverse GET requests;
search and inspect entity metadata.

No POST, PATCH, DELETE, upload, remediation or workspace-mutation tools are registered.

Current Dataverse execution uses an Azure CLI tenant session. For tenants without an Azure subscription, sign in with:

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

The Hub shows the selected environment, MCP mode, available tool count, lifecycle, authentication guidance and traffic-light health state.

Business Architecture Understanding

The user-facing capability is Business Architecture Understanding. Its underlying Operational Workflow Intelligence engine separates:

Core Domain — the principal business, service, clinical, case, plan or request concepts;
Coordination — journey, referral, 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.

dvqr_discover_operational_anchors returns explainable conclusions rather than an unexplained entity ranking. Each major conclusion can include:

confidence and evidence provenance;
why DVQR reached the conclusion;
metadata evidence and bounded runtime observations;
explicit runtime-verification requirements;
a plain-English architectural narrative;
an onboarding-oriented “If I joined tomorrow” summary.

Capability classification remains metadata-derived. Strong structural evidence does not prove that a particular record participated until runtime evidence is collected.

Evidence-guided workflow discovery

DVQR continues bounded exploration through materially different workflow families rather than stopping at the shortest direct task relationship. Runtime probing keeps separate conclusions for:

the metadata recommendation;
runtime-observed workflow;
accessible paths with no matching data;
permission-limited evidence;
incomplete coverage when a probe budget is exhausted.

Runtime evidence is investigation-scoped and never overwrites metadata confidence.

Try these prompts

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, identify the operational anchors around Account and explain why each was ranked.

Using DV Quick Run, investigate this Contact from the highest-ranked operational anchor and separate metadata recommendation from runtime-observed workflow.

What the Free MCP experience looks like

Find related Dataverse tables deterministically

DV Quick Run MCP deterministic metadata search

Query Dataverse in natural language

DV Quick Run MCP natural-language OData execution

Explain OData in plain English

DV Quick Run MCP OData explanation

Without DV Quick Run	With DV Quick Run MCP
Remember OData syntax	Ask in plain English
Browse metadata manually	Search related tables deterministically
Interpret logical names yourself	Receive structured, metadata-grounded context
Risk broad or unsupported metadata filters	Use bounded DVQR tools with explicit ranking

Core capabilities

Local MCP: business architecture understanding, deterministic metadata discovery, relationship intelligence, natural-language OData, bounded GET execution and explainability.
Query workbench: OData, FetchXML, $batch, Query-by-Canvas, CodeLens, Query Doctor, metadata-aware suggestions and preview-first Smart PATCH.
Investigation: Result Viewer, Guided Traversal, Execution Insights, Operational Profiles, Operational Context and Access Context.
Evidence and comparison: Evidence Workspace, Snapshot Library, Cross-Environment Diff, Timeline Reconstruction, Audit Evidence and investigation handoff reports.
Understanding: Query Understanding, Cross Diff Understanding, Timeline Understanding and evidence-backed Mini RCA.
DV ForgeLab handoffs: DVBUR plus DVAF, DVIM, DVCE and DVEVM reconstruction artifacts, while investigation and reconstruction remain separate concerns.

🌐 Website & Interactive Demo

Official website:

https://www.dvquickrun.com

The website includes:

product overview
roadmap direction
operational investigation philosophy
feature walkthroughs
marketplace/install links
Free / Pro / Offline pricing and activation guidance
interactive mock HTML comparison reports demonstrating DV Quick Run investigation workflows
sample Diff Findings Summary, Cross Diff Understanding, Timeline Understanding, Mini RCA, Timeline Findings Summary, Investigation Handoff, Timeline Investigation Handoff, audit-aware report flows, and Reconstruction Artifact handoff flows

The interactive HTML demo helps illustrate:

grouped operational drift investigation
inline evidence continuation
operational verification workflows
Findings / Verification / Handoff investigation flow
Timeline Reconstruction, interval graph, first-observed drift, audit evidence enrichment, and reconstruction artifact report flows
dense enterprise comparison readability
operational investigation continuity
report export mental models for Cross Diff Understanding, Timeline Understanding, Mini RCA, Diff Findings Summary, Timeline Findings Summary, Investigation Handoff, and Timeline Investigation Handoff workflows

without requiring a live Dataverse environment.

🚀 14-day Pro Trial

DV Quick Run surfaces user-facing 14-day Pro Trial messaging so teams can try the full Pro investigation workflow before choosing Pro.

Trial messaging highlights:

Cross-Environment Diff
Timeline Reconstruction
Audit Evidence
Reconstruction Artifact exports
HTML/PDF investigation reports

Trial and billing configuration is handled externally by the store/licensing provider. DV Quick Run only needs a valid Pro entitlement to unlock Pro features.

⚡ Quick Start

Run DV Quick Run normally

Install DV Quick Run.
Configure and select your Dataverse environment.
Run a query such as:
```
contacts?$top=10
```
Open the Hub any time:
```
DV Quick Run: Open Hub
```

Enable Local MCP

Select the Dataverse environment you want DV Quick Run MCP to use.
Run:
```
DV Quick Run: Enable Local MCP Server
```
Sign into the environment tenant with Azure CLI. For a tenant without an Azure subscription:
```
az login --tenant <tenant-id> --allow-no-subscriptions
```
Open GitHub Copilot Chat in VS Code and make sure the DV Quick Run MCP tools are enabled.

Try one of these prompts:

Using DV Quick Run, give me the top 10 Accounts by Revenue.

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

Open DV Quick Run: Open Hub to review the Local MCP traffic-light status, environment, mode, tool count, lifecycle, and authentication guidance.

DV Quick Run remembers MCP enablement per workspace. VS Code starts the local stdio server on demand after restarts or reboots; users do not need to recreate the configuration.

🔐 DV Quick Run Pro Activation

DV Quick Run is available in Free and Pro editions.

Free preserves foundational operational understanding workflows. Pro accelerates advanced operational investigation workflows such as real snapshot comparison, replay continuity, report exports, and investigation handoff workflows.

Learn more:

https://www.dvquickrun.com/pricing

Direct purchase / checkout:

https://dvforgelab.lemonsqueezy.com

To activate Online Pro:

Purchase a DV Quick Run Pro subscription.
Open Visual Studio Code.
Run:
```
DV Quick Run: Activate Pro License
```
Paste the license key from your Lemon Squeezy purchase.

To inspect your current entitlement:

DV Quick Run: License Status

Online recurring subscriptions display as:

Subscription: Active

Eligible Pathfinder licenses show:

DVQR Pathfinder • Early Supporter

Offline Pro customers can import a signed offline license using:

DV Quick Run: Import Offline License

Offline Pro is designed for restricted, disconnected, and air-gapped environments where recurring online validation is not suitable.

🎬 Result Viewer

DV Quick Run Result Viewer

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

Typical workflow:

start simple → run → explore → refine → update safely → refresh → repeat

Result Viewer also acts as an operational launch surface. From a primary row id, you can open Bound Actions on this record to preview compatible entity-bound Actions for that specific row.

The Result Viewer only supplies target row context. Execution still flows through DV Quick Run’s governed preview surface:

row → bound Action preview → explicit confirmation → execution result → investigation context

📦 Result Viewer → DVBUR Artifact Export

DV Quick Run can export Result Viewer records as DVBUR-compatible artifacts for DV ForgeLab's DV Bulk Upsert Runner.

This supports a focused ecosystem workflow:

DVQR investigates
→ exports a DVBUR artifact
→ DVBUR performs focused bulk upsert execution

The export remains explicit and user-triggered.

DV Quick Run does not perform automatic remediation or hidden bulk updates. It prepares an artifact from observed Result Viewer data so the execution workflow can remain separated in DV Bulk Upsert Runner.

✨ Key Features

🔎 Run & Explore Queries

Run Dataverse OData and FetchXML directly in VS Code
View results in an interactive table or JSON
Sort, filter, inspect, copy, and act on data inline
Use Ctrl+Enter to run the query under your cursor

🧭 DV Quick Run Hub

The Hub provides a calm orientation surface for operational investigation workflows.

It helps you:

understand current investigation context
see whether a Result Viewer context is active, recoverable, historical, or stale
reopen recoverable Result Viewer sessions
track selected $batch sub-results
pivot to related investigation surfaces
discover Snapshot Library and operational comparison workflows
open DVQR GitHub Discussions for feedback, bugs, workflow ideas, and roadmap conversation
avoid stale context after environment switches

The Hub is optional. It does not take over the workflow; it helps you recover orientation when you need it.

🔐 Access Context

Access Context helps you investigate bounded operational identity participation without leaving VS Code.

You can investigate:

users
application/service identities
teams
roles
business units

Access Context can surface:

business-unit context
direct role participation
team participation
inherited participation
member composition
role participation
notable operational participants
raw verification evidence

It is designed for operational investigation, not security administration.

Access Context does not:

simulate RBAC
calculate effective record access
generate privilege matrices
infer security risk
perform recursive environment-wide topology crawling

Access Context can be launched from:

Command Palette
DV Quick Run Hub
Result Viewer row actions

Common Result Viewer continuations include:

systemusers.systemuserid       → Check User Access Context
systemusers.systemuserid       → Check Application User Context
teams.teamid                   → Check Team Access Context
roles.roleid                   → Check Role Access Context
businessunits.businessunitid   → Check Business Unit Context

Access Context remains summary-first, searchable, exportable, and bounded to the current investigation subject.

🧩 Query-by-Canvas

Query-by-Canvas is DV Quick Run’s preview-first refinement model.

Start simple, then refine from results:

contacts
→ add $top
→ add $select
→ filter by value
→ rerun

Supported refinement paths include:

add fields
filter by value
preview query changes
apply safely
rerun and verify

🔗 Guided Traversal

Guided Traversal helps you navigate relationships across Dataverse tables.

Use it to:

find paths between entities
traverse using real returned rows
continue exploration step-by-step
understand relationship routes visually
replay traversal flows as $batch

🕸️ Relationship Graph

DV Quick Run includes a dedicated Relationship Graph workspace for exploring Dataverse entity relationships.

Relationship Graph can surface:

relationship counts by type
Many-to-One relationships
One-to-Many relationships
Many-to-Many relationships
navigation properties
target entities
relationship schema names

The workspace supports:

live search
match highlighting
next/previous navigation
automatic match focus
exact text export
exact text copy

Relationship Graph is designed for operational metadata understanding.

It does not perform recursive graph analysis, dependency impact analysis, or deployment validation.

Relationship Graph can be launched directly from:

Result Viewer
→ View Relationships

The original relationship artifact remains available through:

Save Exact Text
Copy Exact Text

so visual exploration and exported evidence remain consistent.

📦 `$batch` Workflows

Run multiple related queries together using $batch.

Useful for:

validating several endpoints together
investigating related tables in one execution
replaying Guided Traversal routes
comparing related results without manual switching

The Hub tracks selected $batch sub-results so investigation context stays aligned with the selected response.

✏️ Smart PATCH

Smart PATCH lets you update Dataverse records directly from the Result Viewer using a preview-first workflow.

It supports:

previewing PATCH payloads before execution
metadata-aware boolean and choice inputs
automatic result refresh after update
guardrails for expanded or unsafe update contexts

📊 Execution Insights

Understand what is happening behind your Dataverse queries without leaving VS Code.

DV Quick Run surfaces execution behaviour across plugins, async operations, workflows, and Power Automate-related context.

It can help identify:

slow execution
failed or waiting async operations
repeated execution patterns
nested plugin behaviour
correlation/request-linked runtime evidence

Primary Signal 1

Execution Insights prioritises the strongest signal first, then keeps supporting evidence available for deeper investigation.

Guided Investigation 1

🧭 Operational Profiles

Operational Profiles help you understand the operational footprint of a Dataverse entity before diving into deeper troubleshooting.

Profiles can surface:

plugin orchestration density
relationship complexity
metadata footprint
async participation
Power Automate involvement
workflow participation
managed-state context
comparison-ready snapshot evidence for operational comparison and Timeline Reconstruction workflows

Operational Profiles are:

entity-scoped
user-triggered
evidence-backed
bounded
advisory-only

They help identify good investigation starting points without implying speculative root cause.

Operational Profile snapshot export is capability-aware: Free keeps operational understanding available, while Pro unlocks snapshot persistence, comparison workflows, and Timeline Reconstruction workflows.

🔄 Evidence Workspace, Snapshot Library, Timeline Reconstruction & Cross-Environment Diff

DV Quick Run includes a local DV ForgeLab Evidence Workspace for organising investigation artifacts and reconstruction handoffs:

.dvforgelab
├─ dvaf
│  └─ exports
└─ dvqr
   ├─ comparisons
   ├─ reports
   └─ snapshots

Snapshot Library coordinates saved operational investigation snapshots and acts as the central console for comparison and timeline reconstruction.

It supports:

source and target snapshot selection
3+ snapshot timeline reconstruction selection
grouping snapshots by environment and subject
latest-vs-previous snapshot comparison
snapshot search and filtering
grouped recent comparison history
replayable recent comparisons
bounded comparison-history rendering
comparison-history cleanup without deleting snapshots
mock snapshot exploration in Free / Pro Preview mode
built-in TIMELINE-MOCK snapshots for timeline preview
real snapshot workflows in Pro

DV Quick Run uses the selected evidence shape to guide the workflow:

2 compatible snapshots
→ Operational Comparison

3+ compatible snapshots from the same environment and entity
→ Operational Timeline Reconstruction

different environments
→ Cross-Environment Diff

Timeline Reconstruction is intentionally restricted to same-environment, same-entity snapshots.

Cross-environment timelines are blocked because they would mix environment comparison with historical evidence reconstruction.

⏱️ Operational Timeline Reconstruction

Operational Timeline Reconstruction helps investigators understand how operational evidence evolved across multiple snapshots.

Select:

3+ snapshots
same environment
same entity / subject

DV Quick Run reconstructs:

snapshot-bounded intervals
first-observed drift windows
Timeline Graph
provider distributions
significance distributions
Timeline Trust
Timeline Findings Summary reports
Timeline Investigation Handoff reports
optional Audit Evidence Enrichment where Dataverse audit rows are available

Timeline Reconstruction can show provider-backed operational drift across:

Operational Profiles
Plugin Step Runtime Behaviour
Solution Participation
Workflow / Automation Participation
Identity Participation
Relationship Metadata Drift
Column Metadata Drift
Choice Metadata Drift
Entity Configuration Drift

Timeline findings answer:

What changed?
When was it first observed between snapshot captures?
Which evidence provider detected it?
What supporting evidence exists?

Timeline findings do not claim:

exact change time
root cause
human responsibility
remediation status
operational authority

📈 Timeline Graph

Timeline Graph provides a visual reconstruction of selected snapshot intervals.

It shows:

ordered snapshot progression
interval boundaries
event density by interval
first-observed drift concentration
clickable interval navigation in the timeline investigation surface

This makes timeline investigations easier to scan before reviewing detailed findings.

📄 Timeline Reports

Timeline Reconstruction can export dedicated timeline artifacts:

Timeline Findings Summary — executive-style timeline summary focused on first-observed drift, interval distribution, provider contribution, significance mix, timeline trust, and top findings
Timeline Investigation Handoff — evidence-first handoff report for investigation continuity, sharing, escalation, and operational review

Timeline reports are available as branded HTML/PDF artifacts.

They preserve:

timeline range
snapshot count
interval count
event count
snapshot-bounded interval timeline
provider distribution
significance distribution
top timeline events
evidence references
audit evidence where explicitly queried before export
trust and verification boundaries

🔍 Audit Evidence Enrichment

Audit Evidence Enrichment adds optional Dataverse audit context to Timeline Reconstruction and Cross-Environment Diff investigations.

When audit evidence is queried, DV Quick Run searches within the relevant snapshot-bounded investigation window and renders matching audit rows alongside the related finding.

Audit evidence may include:

recorded timestamp
recorded user / actor where available
operation and action labels
changed attributes for supported entity update payloads
security or relationship association information
partially interpreted Dataverse audit payloads
raw payload preservation in HTML reports

Audit Evidence Enrichment is intentionally bounded:

Audit evidence enriches investigation context.
Audit evidence does not establish causality, deployment correctness, remediation status, or operational authority.

Dataverse audit payload interpretation is experimental. Some payloads may be partially interpreted or preserved as raw evidence so users can submit edge cases through Feedback.

🧩 Reconstruction Artifacts

Reconstruction Artifacts preserve supported source-side metadata drift as explicit DVAF handoff files.

Eligible Column Metadata Drift findings can export .dvaf.json artifacts into:

.dvforgelab/dvaf/exports

These artifacts can be imported into DV Attribute Factory for preview-first reconstruction of supported metadata.

DVQR may export reconstruction intent for supported source-side definitions such as:

text and multiline text
numeric and currency columns
date columns
choice columns with captured option values
lookup columns with captured target metadata

DVQR blocks export for non-standalone companion attributes, shadow columns, and source metadata that is not valid for create.

Reconstruction Artifacts are handoffs only:

DVQR investigates and exports reconstruction intent.
DVAF validates, previews, and creates supported metadata.
Humans retain operational authority.

Global choice lifecycle reconstruction is intentionally left for DV Choice Editor rather than DV Attribute Factory.

Operational Snapshot Library

The Snapshot Library provides Evidence Workspace management, snapshot selection, timeline reconstruction, cross-environment comparison, and built-in TIMELINE-MOCK samples for free timeline previews.

🎭 Free Timeline Preview

DV Quick Run includes built-in TIMELINE-MOCK snapshots.

Free users can:

explore timeline workflows
select mock timeline snapshots
generate sample Timeline Graphs
export sample Timeline Findings Summary reports
export sample Timeline Investigation Handoff reports
understand first-observed drift analysis without setup

Real operational timeline reconstruction remains Pro capability-aware.

Cross-Environment Diff

Cross-Environment Diff remains the workflow for comparing snapshots across different environments.

Cross Diff Explain is available from the comparison Reports menu. It opens as Markdown Preview and saves under the DVQR reports workspace so investigators can understand the comparison before drilling into detailed evidence.

Comparison reports preserve scope awareness so exported artifacts clearly identify the operational subject being compared, for example:

Cross-Environment Diff: Contact • DEV → SIT

When snapshots represent different operational subjects, DV Quick Run warns before continuing so users do not accidentally treat unrelated subjects as meaningful operational drift.

Dense comparison reports use grouped operational surfaces so investigations remain readable at enterprise scale. High-signal drift remains visible first, while lower-priority evidence is grouped with:

classification rationale
evidence summaries
representative drift signals
operational-priority explanation
full JSON/HTML evidence continuity

Comparison reports also support interactive operational verification workflows. Evidence rows can open inline investigation context directly from the report, including bounded live pivots where DV Quick Run can safely query the active Dataverse environment.

Inline evidence continuation supports:

solution participation evidence
identity/team/role participation evidence
workflow and automation participation evidence
grouped representative signals
custom/publisher-prefixed entity metadata context
captured context-only evidence with explanatory fallback wording

The comparison workspace is organised around:

Findings — review the operational drift evidence
Verification — track what has been reviewed, externally checked, or still needs follow-up
Handoff — preserve operational notes and review posture for human investigation continuity

Review and verification state is local to the comparison workflow and remains investigation-oriented. Marking something reviewed does not imply remediation, correctness, access authority, or root-cause certainty.

Comparison reports can be exported as dedicated operational artifacts:

Cross Diff Explain — Markdown Preview briefing that summarises confidence, key operational changes, affected areas, risk posture, investigation path, technical breakdown, and raw comparison references
Diff Findings Summary — concise operational briefing focused on strongest drift signals, executive summary, significance distribution, provider distribution, snapshot trust, and top operational findings
Investigation Handoff — verification-oriented handoff package focused on outstanding operational review, grouped evidence continuity, review posture, and external follow-up context
HTML reports — readable, branded report exports that preserve operational hierarchy and evidence-backed summaries
PDF reports — branded, watermarked, print-friendly exports for review packs, CAB discussions, handoff, escalation, and stakeholder communication

The comparison toolbar groups report exports under:

Reports

so standard evidence exports and report artifacts stay distinct:

Save JSON / Save MD / Save HTML / Reports / Reset Review State

The comparison and timeline models are intentionally observational:

DVQR observes operational drift.
DVQR does not fix operational drift.

DVQR reconstructs observed evidence.
DVQR does not reconstruct historical certainty.

Operational comparison and timeline reconstruction are not deployment tooling, remediation automation, root-cause certainty, or environment authority.

They are designed to help you understand:

what changed
when drift was first observed
where operational density shifted
which providers contributed evidence
which drift signals may deserve follow-up investigation
whether runtime plugin or automation participation differs between snapshots
whether platform-layer drift is low-priority context or relevant to the investigation
whether a comparison or timeline is scope-aligned before treating drift as meaningful

Audit Evidence Enrichment is available in v0.13.1.

Current coverage includes:

snapshot-bounded audit retrieval
Timeline Reconstruction audit enrichment
Cross-Environment Diff audit enrichment
security and relationship association interpretation
audit-aware HTML/PDF exports
raw payload preservation for partially interpreted audit rows

Future roadmap direction includes expanded audit payload interpretation, ownership transfer decoding, platform-operation decoding, custom association decoding, richer attribute-level audit correlation, and timeline-assisted root-cause guidance while preserving human verification boundaries.

🧠 Explain & Investigation Intelligence

Use Explain to turn Dataverse technical evidence into human-readable investigation briefings.

Supported Explain surfaces include:

Query Explain — explains OData and FetchXML query structure, intent, risk, recommendations, and raw query mechanics
Cross Diff Explain — explains cross-environment comparison evidence, confidence basis, key operational changes, investigation priority, and recommended investigation path

Use Explain Query to turn Dataverse query structure into operational investigation understanding. Investigation Intelligence produces investigation summaries, confidence assessment, investigation pattern teaching, evidence-backed guidance, clause analysis, and verification recommendations. Query Doctor participates as an advisory contributor in the same pipeline.

Supported workflows include:

break down query structure
understand filters, sorting, expands, and selected fields
identify missing $top or $select
preview suggested improvements
apply safe refinements through preview-first workflows

🔍 Investigate Record

Investigate a record from a GUID or result context.

Useful for:

primary keys
surfaced business GUID fields
record interpretation
relationship exploration
suggested follow-up queries

🧬 Metadata Intelligence

DV Quick Run uses Dataverse metadata to improve query building and investigation.

Features include:

field metadata hover
choice label resolution
relationship awareness
entity set resolution
preview-first filter refinement

🧩 Capability Explorer & Governed Operational Execution

DV Quick Run includes a metadata-backed Capability Explorer for discovering, understanding, previewing, and executing supported Dataverse operational capabilities.

It helps identify:

executable vs inspect-only Custom APIs
Functions vs Actions
bound vs unbound operations
public vs private capability visibility
parameter complexity and preview support
OData execution eligibility
Action execution support state
AI-related execution policy state
governed operational execution context

Capability Explorer supports:

entity-bound operational execution
operational capability discovery
metadata-backed execution validation
preview-first Function execution
preview-first eligible unbound Action execution
preview-first entity-bound Action execution
preview-first collection-bound Action execution
Result Viewer row-context bound Action previews
metadata-aware bound route generation
explicit target-row execution workflows
simple parameter request shaping
explicit execution confirmation
access-aware discovery behaviour under restricted permissions
execution diagnostics
execution result inspection
Capability Execution Insights continuation
structured operational investigation

Capability Explorer now presents Action execution using a clearer support taxonomy:

Preview-ready — all discovered parameters can be represented safely in the preview foundation
Partially preview-ready — some parameters can be previewed, while others remain inspect-only
Ready to run — the Action is metadata-valid, OData-exposed, preview-ready, and executable after confirmation
Run with caution — the Action is executable, but DV Quick Run detected operational-impact or governance signals requiring extra review
Preview request only — a request template can be generated, but no Dataverse operation will be executed
Inspect only — the operation remains discoverable and inspectable, but cannot be executed safely in the current release boundary

This keeps unsupported or private operations useful for investigation without making them appear broken or silently executable.

Capability Explorer Function Preview

🔗 Entity-Bound Action Execution

DV Quick Run can now execute supported entity-bound Dataverse Actions using explicit target-row context.

Capability Explorer automatically:

resolves executable bound OData routes
generates preview-ready request shapes
validates execution eligibility
preserves preview-first execution trust semantics
captures execution diagnostics and operational investigation context

Bound execution remains:

metadata-aware
explicit
environment-bound
confirmation-driven
investigation-oriented

Entity-Bound Action Execution

Execution results preserve:

request shape visibility
execution identifiers
execution diagnostics
captured operational investigation context

Entity-Bound Action Result

🧭 Result Viewer Bound Actions

Result Viewer rows can now open compatible bound Actions directly from the row action menu.

Use Bound Actions on this record to move from a returned Dataverse row into a governed Action preview without manually constructing the bound OData route.

Behaviour:

the row supplies target entity and row id context
DV Quick Run resolves the bound OData route from metadata
execution preview remains the authority boundary
execution still requires explicit confirmation
request and response context are captured as investigation evidence

If Custom API discovery is restricted, DV Quick Run keeps the action visible but explains why it is unavailable instead of showing noisy failures.

🔒 Access-Aware Capability Discovery

Capability Explorer now handles restricted Custom API discovery access as an expected enterprise condition.

When access is restricted, DV Quick Run opens a calm restricted-access surface instead of treating the launch as a tool failure.

Where available, DV Quick Run surfaces actionable remediation details:

principal user
missing privilege
required entity
HTTP status

This keeps enterprise environments understandable even when security roles intentionally restrict Custom API metadata visibility.

The capability model is intentionally:

metadata-driven
preview-first
explicit
investigation-oriented
execution-safe
governance-aware

Execution is validated against the Dataverse OData $metadata surface before supported Functions and eligible unbound Actions can run.

The governing model is:

Custom API metadata = discovery truth
OData metadata = execution exposure truth
bound route metadata = execution route truth
active environment = execution authority boundary

AI-related operations are governed separately. By default, DV Quick Run blocks AI-related execution:

"dvQuickRun.execution.aiPolicy": "deny"

Set the policy to allow only when AI-related execution is intentionally permitted:

"dvQuickRun.execution.aiPolicy": "allow"

When AI execution is allowed, DV Quick Run still surfaces amber advisory warnings because generated responses may be inaccurate, incomplete, non-deterministic, or unsuitable for direct operational decisions without human review.

🌍 Environment Support

Work across configured Dataverse environments such as DEV, UAT, SIT, and PROD.

DV Quick Run supports:

active environment selection
environment-aware metadata caching
safe environment switching
investigation context reset on environment change

🛡 Guardrails

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

It detects or guards against risky situations such as:

missing $top
broad result analysis
unsafe PATCH contexts
unsupported expanded-field updates
stale investigation context
stale execution authority after environment changes
unavailable execution evidence
unsupported or inspect-only Custom API execution
private/internal Custom APIs remaining preview-request only
restricted Custom API discovery access
unavailable Result Viewer bound Action discovery
unsupported or complex Action parameter shapes
high-risk Actions requiring clearer caution semantics
AI-related execution blocked by default
AI-generated content requiring human review
cross-environment investigation leakage
operational-context overclaiming beyond bounded evidence
effective-access claims from Access Context participation evidence
causal claims from solution, ownership, access, or actor participation
treating operational comparison as deployment authority
treating snapshot evidence as remediation instruction
treating mismatched comparison subjects as equivalent operational drift without explicit user awareness
reconstructing timelines from mixed-environment snapshots
treating first-observed timeline windows as exact historical change times
replay-history cleanup deleting underlying snapshots
hidden cross-environment scans or automatic drift verification
treating inline evidence continuation as remediation or proof of root cause
treating review-state completion as operational correctness
treating exported reports, Explain surfaces, or PDFs as approval, certification, root-cause proof, timeline certainty, or remediation authority
treating audit evidence as causality, deployment correctness, remediation proof, or operational authority
assuming unknown Dataverse audit payloads are fully interpreted when DVQR marks them as experimental or partially interpreted
treating DVAF reconstruction artifacts as proof that the source is correct or the target is wrong
treating reconstruction exports as automatic remediation instructions
exporting shadow/system companion attributes as standalone reconstruction candidates

Execution-capable workflows are designed around:

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

DV Quick Run does not treat generated or AI-assisted responses as operational truth. AI-related output should be reviewed before being used for operational decisions.

💬 Community & Feedback

DV Quick Run includes GitHub Discussions entry points from the Hub and operational comparison surfaces.

Use DVQR Discussions to:

report bugs
suggest features
share workflow feedback
discuss operational investigation patterns
propose comparison providers
submit audit payload edge cases
follow roadmap direction

GitHub Discussions:

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

Official website:

https://www.dvquickrun.com

👥 Who Is This For?

Dataverse / Dynamics 365 developers
Power Platform engineers
Integration / API developers
Support engineers investigating Dataverse execution behaviour
Consultants working across complex Dataverse environments

💡 Why DV Quick Run?

Because the fastest Dataverse workflow is:

write → run → explore → refine → investigate → reconstruct evidence → verify

…without leaving your editor.

DV Quick Run is designed to reduce tool switching while keeping investigation, operational context, and execution workflows explicit, bounded, governed, and trustworthy.

🔧 Development

npm install
npm run compile

Press F5 to run the extension.

📜 License

MIT License

Open-Core Model

DV Quick Run follows an open-core model.

The MIT-licensed core preserves foundational operational understanding workflows for Dataverse.

Commercial Pro capabilities focus on advanced operational acceleration workflows such as:

Cross-Environment Diff
Cross Diff Explain
Timeline Reconstruction
Timeline Findings Summary Reports
Timeline Investigation Handoff Reports
Audit Evidence Enrichment
Audit-Aware HTML/PDF Report Exports
Reconstruction Artifact Export
DVAF Reconstruction Handoff
Runtime Behaviour Drift
Identity Participation Drift
Snapshot Replay
Comparison Report Export Workflows
Investigation Handoff Exports
Result Viewer → DVBUR Artifact Export
Online Pro Activation
Offline Pro Licensing
Capability-Aware Acceleration Workflows

The public repository intentionally excludes proprietary Pro implementation modules.

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

Repository Structure

/src/core
MIT open-core functionality

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

Developer MCP verification

The packaged extension owns normal MCP registration and lifecycle. Repository contributors can also run the stdio server directly after npm install and npm run compile:

npm run mcp:start

The process remains silent while waiting for an MCP client. For interactive inspection, use:

npm run mcp:inspect

Azure CLI authentication must already be available through az login. The Free MCP catalogue remains deterministic and read-only; it includes metadata search, capability discovery, relationship intelligence, OData explanation and bounded GET execution without mutation tools.

DV Quick Run

DV ForgeLab

DV Quick Run

What is DV Quick Run?

Custom API Intelligence — v0.15.7

Local MCP — Business Architecture Understanding

Business Architecture Understanding

Evidence-guided workflow discovery

Try these prompts

What the Free MCP experience looks like

Core capabilities

🌐 Website & Interactive Demo

🚀 14-day Pro Trial

⚡ Quick Start

Run DV Quick Run normally

Enable Local MCP

🔐 DV Quick Run Pro Activation

🎬 Result Viewer

📦 Result Viewer → DVBUR Artifact Export

✨ Key Features

🔎 Run & Explore Queries

🧭 DV Quick Run Hub

🔐 Access Context

🧩 Query-by-Canvas

🔗 Guided Traversal

🕸️ Relationship Graph

📦 $batch Workflows

✏️ Smart PATCH

📊 Execution Insights

🧭 Operational Profiles

🔄 Evidence Workspace, Snapshot Library, Timeline Reconstruction & Cross-Environment Diff

⏱️ Operational Timeline Reconstruction

📈 Timeline Graph

📄 Timeline Reports

🔍 Audit Evidence Enrichment

🧩 Reconstruction Artifacts

🎭 Free Timeline Preview

Cross-Environment Diff

🧠 Explain & Investigation Intelligence

🔍 Investigate Record

🧬 Metadata Intelligence

🧩 Capability Explorer & Governed Operational Execution

🔗 Entity-Bound Action Execution

🧭 Result Viewer Bound Actions

🔒 Access-Aware Capability Discovery

🌍 Environment Support

🛡 Guardrails

💬 Community & Feedback

👥 Who Is This For?

💡 Why DV Quick Run?

🔧 Development

📜 License

Open-Core Model

Repository Structure

Developer MCP verification

📦 `$batch` Workflows