setup-datamodel

name	setup-datamodel
description	Creates Dataverse tables, columns, and relationships for a Power Pages site based on a data model proposal. Use when the user wants to set up the data model, create database tables, or build the Dataverse schema for their site.
user-invocable	true
allowed-tools	Read, Write, Bash, Grep, Glob, AskUserQuestion, Task, TaskCreate, TaskUpdate, TaskList, mcp__plugin_power-pages_microsoft-learn__microsoft_docs_search, mcp__plugin_power-pages_microsoft-learn__microsoft_code_sample_search, mcp__plugin_power-pages_microsoft-learn__microsoft_docs_fetch
model	opus

Plugin check: Run node "${PLUGIN_ROOT}/scripts/check-version.js" — if it outputs a message, show it to the user before proceeding.

Set Up Dataverse Data Model

Guide the user through creating Dataverse tables, columns, and relationships for their Power Pages site. Follow a systematic approach: verify prerequisites, obtain a data model (via AI analysis or user-provided diagram), review and approve, then create all schema objects via OData API.

Core Principles

• Never create without approval: Always present the full data model proposal and get explicit user confirmation before making any Dataverse changes.
• Use TaskCreate/TaskUpdate: Track all progress throughout all phases — create the todo list upfront with all phases before starting any work.
• Resilient execution: Refresh tokens proactively, check for existing tables before creating, and report failures without automated rollback.

Initial request: $ARGUMENTS

---

Phase 1: Verify Prerequisites

Goal: Confirm PAC CLI authentication, acquire an Azure CLI token, and verify API access

Actions:

1. Create todo list with all 8 phases (see Progress Tracking table)
2. Follow the prerequisite steps in ${PLUGIN_ROOT}/references/dataverse-prerequisites.md to verify PAC CLI auth, acquire an Azure CLI token, and confirm API access. Note the environment URL as <envUrl> for subsequent script calls.

Output: Verified PAC CLI auth, valid Azure CLI token, confirmed API access, <envUrl> noted

---

Phase 2: Choose Data Model Source

Goal: Determine whether the user will upload an existing ER diagram or let AI analyze the site

Actions:

🚦 Gate (plan · setup-datamodel:2.source): Decide whether the user uploads an existing ER diagram or the data-model-architect agent infers the model. Choice routes the rest of the skill into Path A vs Path B.

Trigger: Entering Phase 2. Why we ask: Auto-picking either path can run a multi-minute architect agent against the wrong intent (Path B) or skip Dataverse-existence checks (Path A). Cancel leaves: Nothing — no Dataverse calls made yet.

1. Ask the user how they want to define the data model using the AskUserQuestion tool:

   Question: "How would you like to define the data model for your site?"

   Option	Description
   Upload an existing ER diagram	Provide an image (PNG/JPG) or Mermaid diagram of your existing data model
   Let the Data Model Architect figure it out	The Data Model Architect will analyze your site's source code and propose a data model automatically

2. Route to the appropriate path:

Path A: Upload Existing ER Diagram

If the user chooses to upload an existing diagram:

1. Ask the user to provide their ER diagram. Supported formats:

   • Image file (PNG, JPG) — Use the Read tool to view the image and extract tables, columns, relationships, and cardinalities from it
   • Mermaid syntax — The user can paste Mermaid ER diagram text directly in chat
   • Text description — A structured list of tables, columns, and relationships

2. Parse the diagram into the same structured format used by the data-model-architect agent:

   • Publisher prefix (ask the user, or retrieve from the environment via pac env who)
   • Table definitions: logicalName, displayName, status (new/modified/reused), columns, relationships
   • Column definitions: logicalName, displayName, type, required
   • Relationship definitions: type (1:N or M:N), referenced/referencing tables

3. Query existing Dataverse tables (same as Phase 3 would) to mark each table as new, modified, or reused.

4. Generate a Mermaid ER diagram from the parsed data (if the user provided an image or text) for visual confirmation.

5. Proceed directly to Phase 4: Review Proposal with the parsed data model.

Path B: Let the Data Model Architect Figure It Out

If the user chooses to let the Data Model Architect figure it out, proceed to Phase 3: Invoke Data Model Architect (the existing automated flow).

Output: Data model source chosen and, for Path A, parsed data model ready for review

---

Phase 3: Invoke Data Model Architect

Goal: Spawn the data-model-architect agent to autonomously analyze the site and propose a data model

Actions:

1. Use the Task tool to spawn the data-model-architect agent. This agent autonomously:

   • Analyzes the site's source code to infer data requirements
   • Queries existing Dataverse tables via OData GET requests
   • Identifies reuse opportunities (reuse, extend, or create new)
   • Proposes a complete data model with an ER diagram

2. Spawn the agent:

   Task tool:
     subagent_type: general-purpose
     prompt: |
       You are the data-model-architect agent. Follow the instructions in
       the agent definition file at:
       ${PLUGIN_ROOT}/agents/data-model-architect.md
   
       Analyze the current project and Dataverse environment, then propose
       a complete data model. Return:
       1. Publisher prefix
       2. Table definitions (logicalName, displayName, status, columns, relationships)
       3. Mermaid ER diagram
   

3. Wait for the agent to return its structured proposal before proceeding.

Output: Structured data model proposal from the agent (publisher prefix, table definitions, ER diagram)

---

Phase 4: Review Proposal

Goal: Present the data model proposal to the user and get explicit approval before creating anything

Actions:

4.1 Present Proposal

Present the data model proposal directly to the user as a formatted message, including:

• Publisher prefix
• All proposed tables with columns (logical names + display names)
• Relationship descriptions
• Mermaid ER diagram
• Which tables are new vs. modified vs. reused

4.2 Get User Approval

🚦 Gate (plan · setup-datamodel:4.2.approval): Final sign-off on the data model proposal before any Dataverse write. Cancel here stops the skill with zero side effects.

Trigger: Phase 4.1 rendered the proposal (tables, columns, relationships, ER diagram). Why we ask: Tables and columns get created in Dataverse against the user's actual schema intent — column types and relationship cardinalities are awkward to undo. Cancel leaves: Nothing — no EntityDefinitions POST yet, no .datamodel-manifest.json write.

Use AskUserQuestion to get approval:

Question	Header	Options
Does this data model look correct?	Data Model Proposal	Approve and create tables (Recommended), Request changes, Cancel

• If "Approve and create tables (Recommended)": Proceed to Phase 5
• If "Request changes": Ask what they want changed, modify the proposal, and re-present for approval
• If "Cancel": Stop the skill

Only proceed to creation after explicit user approval.

Output: User-approved data model proposal

---

Phase 5: Pre-Creation Checks

Goal: Refresh the token, verify what already exists in Dataverse, and build the creation plan to avoid duplicates

Actions:

5.1 Refresh Token

Re-acquire the auth token (tokens expire after ~60 minutes):

node "${PLUGIN_ROOT}/scripts/verify-dataverse-access.js" <envUrl>

5.2 Query Existing Tables

For each table in the approved proposal marked as new, check whether it already exists:

node "${PLUGIN_ROOT}/scripts/dataverse-request.js" <envUrl> GET "api/data/v9.2/EntityDefinitions(LogicalName='<table_logical_name>')"

• If 404: Table does not exist, proceed to create it
• If 200: Table already exists — skip creation, warn the user

For tables marked as modified, verify the table exists (it should) and check which columns are missing.

5.3 Build Creation Plan

From the pre-creation checks, build a list of:

• Tables to create (new tables that don't exist yet)
• Columns to add (new columns on existing/modified tables)
• Relationships to create
• Tables/columns to skip (already exist)

Inform the user of any skipped items.

Output: Finalized creation plan with tables, columns, and relationships to create or skip

---

Phase 6: Create Tables & Columns

Goal: Create each approved table and its columns using the Dataverse OData Web API

Actions:

Refer to references/odata-api-patterns.md for full JSON body templates.

6.1 Create Tables

For each new table, POST to the EntityDefinitions endpoint:

node "${PLUGIN_ROOT}/scripts/dataverse-request.js" <envUrl> POST "api/data/v9.2/EntityDefinitions" --body '<JSON body from references/odata-api-patterns.md>'

Use the deep-insert pattern to create the table and its columns in a single POST request. See references/odata-api-patterns.md for the complete JSON structure.

6.2 Add Columns to Existing Tables

For tables marked as modified, add new columns one at a time:

node "${PLUGIN_ROOT}/scripts/dataverse-request.js" <envUrl> POST "api/data/v9.2/EntityDefinitions(LogicalName='<table>')/Attributes" --body '<column JSON from references/odata-api-patterns.md>'

6.3 Track Progress

Track each creation attempt and its result (success/failure/skipped). Do NOT attempt automated rollback on failure — report failures and continue with remaining items.

6.4 Refresh Token if Needed

If creating many tables, the dataverse-request.js script handles 401 token refresh automatically. No manual refresh is needed between batches.

Output: All approved tables and columns created (or failures reported)

---

Phase 7: Create Relationships

Goal: Create all relationships between the newly created and existing tables

Actions:

7.1 One-to-Many Relationships

Create lookup columns that establish 1:N relationships:

node "${PLUGIN_ROOT}/scripts/dataverse-request.js" <envUrl> POST "api/data/v9.2/RelationshipDefinitions" --body '<relationship JSON from references/odata-api-patterns.md>'

7.2 Many-to-Many Relationships

Create M:N relationships (intersect tables are created automatically):

node "${PLUGIN_ROOT}/scripts/dataverse-request.js" <envUrl> POST "api/data/v9.2/RelationshipDefinitions" --body '<M:N relationship JSON from references/odata-api-patterns.md>'

7.3 Track Relationship Creation

Track each relationship creation attempt. Report failures without rolling back.

Output: All approved relationships created (or failures reported)

---

Phase 8: Publish & Verify

Goal: Publish all customizations, verify tables exist, write the manifest, and present a summary

Actions:

8.1 Publish Customizations

Publish all customizations so the new tables and columns become available:

node "${PLUGIN_ROOT}/scripts/dataverse-request.js" <envUrl> POST "api/data/v9.2/PublishXml" --body '{"ParameterXml":"<importexportxml><entities><entity>cr123_project</entity><entity>cr123_task</entity></entities></importexportxml>"}'

See references/odata-api-patterns.md for the full PublishXml pattern.

8.2 Verify Tables Exist

For each created table, run a verification query:

node "${PLUGIN_ROOT}/scripts/dataverse-request.js" <envUrl> GET "api/data/v9.2/EntityDefinitions(LogicalName='<table>')?$select=LogicalName,DisplayName"

8.3 Write Manifest

After successful verification, write .datamodel-manifest.json to the project root. This file records which tables and columns were verified to exist, and is used by the validation hook.

{
  "environmentUrl": "https://org12345.crm.dynamics.com",
  "tables": [
    {
      "logicalName": "cr123_project",
      "displayName": "Project",
      "status": "new",
      "columns": [
        { "logicalName": "cr123_name", "type": "String" },
        { "logicalName": "cr123_description", "type": "Memo" }
      ]
    }
  ]
}

Use the Write tool to create this file at <PROJECT_ROOT>/.datamodel-manifest.json. Only include tables and columns that were confirmed to exist in Step 8.2. See ${PLUGIN_ROOT}/references/datamodel-manifest-schema.md for the full schema specification.

8.4 Record Skill Usage

Reference: ${PLUGIN_ROOT}/references/skill-tracking-reference.md

Follow the skill tracking instructions in the reference to record this skill's usage. Use --skillName "SetupDatamodel".

8.5 Present Summary

Present a summary to the user:

Table	Status	Columns	Relationships
cr123_project (Project)	Created	5 columns	2 relationships
contact (Contact)	Reused	1 column added	—
cr123_task (Task)	Created	4 columns	1 relationship

Include:

• Total tables created/modified/reused/failed
• Total columns created/skipped/failed
• Total relationships created/failed
• Any errors encountered with details
• Location of the manifest file (.datamodel-manifest.json)

8.6 Suggest Next Steps

After the summary, suggest:

• Review created tables in the Power Pages maker portal
• Populate tables with sample data for testing: /add-sample-data
• Integrate tables with your site's frontend via Web API: /integrate-webapi
• If the site is not yet built: /create-site
• If the site is ready to deploy: /deploy-site

Output: Published customizations, verified tables, manifest written, summary presented

---

Important Notes

Throughout All Phases

• Use TaskCreate/TaskUpdate to track progress at every phase
• Ask for user confirmation at key decision points (see list below)
• Token refresh is automatic — the dataverse-request.js script handles 401 token refresh and 429/5xx retry internally
• Report failures without rollback — track each creation attempt and continue with remaining items on failure

Key Decision Points (Wait for User)

1. After Phase 2: Data model source chosen (upload vs. AI)
2. After Phase 4: Approve data model proposal before any creation
3. After Phase 5: Acknowledge any skipped items before proceeding
4. After Phase 8: Review summary and choose next steps

Progress Tracking

Before starting Phase 1, create a task list with all phases using TaskCreate:

Task subject	activeForm	Description
Verify prerequisites	Verifying prerequisites	Confirm PAC CLI auth, acquire Azure CLI token, verify API access
Choose data model source	Choosing data model source	Ask user to upload ER diagram or let AI analyze the site
Invoke data model architect	Invoking data model architect	Spawn agent to analyze site and propose data model
Review and approve proposal	Reviewing proposal	Present data model proposal to user, get explicit approval
Pre-creation checks	Running pre-creation checks	Refresh token, query existing tables, build creation plan
Create tables and columns	Creating tables and columns	POST to OData API to create tables and columns
Create relationships	Creating relationships	POST to OData API to create 1:N and M:N relationships
Publish and verify	Publishing and verifying	Publish customizations, verify tables, write manifest, present summary

Mark each task in_progress when starting it and completed when done via TaskUpdate. This gives the user visibility into progress and keeps the workflow deterministic.

---

Begin with Phase 1: Verify Prerequisites