Migrate Canvas App Data Sources to Dataverse

Read the YAML files of the current Canvas App and replace all Power FX data source calls with Dataverse equivalents for the following requirements:

$ARGUMENTS

CRITICAL: Review Guidance First

Before making any changes, you MUST read and internalize the technical reference document:

• ${CLAUDE_PLUGIN_ROOT}/references/TechnicalGuide.md — Technical best practices, control selection, validation workflow, formulas, layout strategies

Read this file before planning any edits.

CRITICAL: Sync the Canvas App First

Before reading or editing any YAML files, call the sync_canvas MCP tool to ensure a local copy of the canvas app YAML is present and up to date. This pulls the current app state from the coauthoring session into local .pa.yaml files.

Only proceed after sync_canvas completes successfully.

Migration Workflow

1. Discover Available Data Sources

Call list_data_sources to enumerate all data sources connected to the current authoring session. This is the authoritative list of Dataverse tables (and other connectors) available for mapping.

After the call completes, share a discovery summary with the user:

Discovery complete. Available data sources ([N] total):

Name	Type	Key Columns
[Table Name]	Dataverse / SharePoint / …	[column names]

For each Dataverse table identified, call get_data_source_schema to retrieve the full column list and Power Fx types. This information is required to map source columns to destination columns accurately.

2. Read the YAML Files

Read every .pa.yaml file produced by sync_canvas. For each file:

• Identify every Power Fx formula that references a non-Dataverse data source (e.g. SharePoint.GetItems, Filter('MyList', …), Patch('MyList', …), LookUp, Collect, ClearCollect, etc.).
• Note the source table/list name, the columns referenced, and the operation type (Filter, Patch, LookUp, Collect, etc.).

3. Build a Column Mapping Plan

Using the schemas retrieved in step 1, produce a mapping table for every data source call found:

Proposed Mapping Plan

Source Expression	Source Column	Dataverse Table	Dataverse Column	Notes
Filter('Orders List', Status = "Open")	Status	cr123_orders	cr123_status	Type match: Text
…	…	…	…	…

Rules for column selection:

• Prefer an exact name match (case-insensitive).
• Fall back to a semantic name match (e.g. Title → cr123_name).
• Flag any column with no clear match as ⚠ needs manual review.
• Respect Power Fx type compatibility — do not map a Text column to a Choices column without an explicit conversion formula.

Present the plan to the user and ask for approval before making any changes:

Does this mapping plan look correct?

• Approve and apply changes
• I'd like to adjust the mapping first

If adjustments are requested, update the mapping plan accordingly and re-present.

4. Apply the Replacements

Once the plan is approved, update every affected .pa.yaml file:

• Replace each source data-call expression with the equivalent Dataverse Power Fx expression using the approved column mapping.
• Keep all UI properties, layout, and non-data formulas unchanged.
• Follow the formula conventions in ${CLAUDE_PLUGIN_ROOT}/references/TechnicalGuide.md (use = prefix, wrap multi-line formulas correctly, etc.).
• Preserve OnVisible initialization patterns — replace collection sources but keep the collection/variable structure if it exists.

Announce progress for each file:

Updating [filename].pa.yaml ([N] of [Total])…

5. Validate

Call compile_canvas after updating all files. Fix any compilation errors before finishing. Report the result:

• On success: > Compilation successful — all replacements are valid.
• On failure: > [N] error(s) found — fixing before finishing. [brief description of each error]

Repeat validate → fix until all files compile clean.

6. Complete

When all files pass validation, present a final summary:

Migration complete.

File	Expressions Replaced	Status
[filename].pa.yaml	[N]	Compiled

Source replaced: [original data source names] Target: [Dataverse table names used] Columns requiring manual review: [list any ⚠ flagged columns, or "none"]