dv-overview

Skill: Overview — What to Use and When

This skill provides cross-cutting context that no individual skill owns: tool capabilities, UX principles, and the skill index. Per-task routing is handled by each skill's WHEN/DO NOT USE WHEN frontmatter triggers — not duplicated here.

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Hard Rules — Read These First

These rules are non-negotiable. Violating any of them means the task is going off-rails.

0. Check Init State Before Anything Else

Before writing ANY code or creating ANY files, check if the workspace is initialized:

ls .env scripts/auth.py 2>/dev/null

• If BOTH exist: workspace is initialized. Proceed to the relevant task.
• If EITHER is missing: Automatically run the connect flow (see the dv-connect skill). Do NOT ask the user whether to initialize — just do it. Do not create your own .env, requirements.txt, .env.example, or auth scripts. The dv-connect skill handles all of this.

Do NOT create requirements.txt, .env.example, or scaffold files manually. The connect flow produces the correct file structure. Skipping it is the #1 cause of broken setups.

1. Python Only — No Exceptions

All scripts, data operations, and automation MUST use Python. This plugin's entire toolchain — scripts/auth.py, the Dataverse SDK, all skill examples — is Python-based.

NEVER:

• Run npm init, npm install, or any Node.js/JavaScript tooling
• Install packages via npm, yarn, or pnpm
• Write scripts in JavaScript, TypeScript, PowerShell, or any language other than Python
• Use @azure/msal-node, @azure/identity, or any Node.js Azure SDK
• Import or reference node_modules/

ALWAYS:

• Use pip install for Python packages
• Use scripts/auth.py for authentication tokens and credentials
• Use the Python Dataverse SDK (PowerPlatform-Dataverse-Client) for data and schema operations
• Use azure-identity (Python) for Azure credential flows

If you find yourself about to run npm or create a package.json, STOP. You are going off-rails. Re-read the python-sdk skill.

2. MCP → SDK → Web API (in that order)

Before writing ANY code, ask: can MCP handle this? If MCP tools are available in your tool list (list_tables, describe_table, read_query, create_record, etc.) and the task is a simple read, query, or small CRUD operation (≤10 records), use MCP — no script needed. Examples: "how many accounts have 'jeff' in the name?", "show me the columns on the contact table", "create an account named Contoso."

If MCP can't handle it (bulk operations, schema creation, multi-step workflows, analytics, or MCP tools aren't available), use the Python SDK — not raw HTTP. This is the most common mistake agents make.

SDK checklist — evaluate EVERY time you write a script:

• Creating/reading/updating/deleting records? → client.records.create(), .get(), .update(), .delete()
• Creating tables or columns? → client.tables.create()
• Creating relationships? → client.tables.create_lookup_field() or client.tables.create_many_to_many_relationship()
• Creating publishers or solutions? → client.records.create("publisher", {...}), client.records.create("solution", {...})
• Bulk operations? → client.records.create(table, [list_of_dicts])
• Querying with $select/$filter/$expand? → client.records.get() with select=, filter=, expand=

If you are about to write import requests or from auth import get_token in a script, STOP. Ask yourself: does the SDK support this operation? If yes, use from auth import get_credential + DataverseClient instead. The get_token() function exists ONLY for the narrow set of operations the SDK does not support.

Raw Web API is ONLY acceptable for: forms, views, global option sets, N:N $ref associations, N:N $expand, $apply aggregation, memo columns, and unbound actions. Everything else MUST use MCP (if available) or the SDK.

Field casing: $select/$filter use lowercase logical names (new_name). $expand and @odata.bind use Navigation Property Names that are case-sensitive and must match $metadata (e.g., new_AccountId). Getting this wrong causes 400 errors. The SDK handles this correctly for @odata.bind keys.

Publisher prefix: Never hardcode a prefix (especially not new). Always query existing publishers in the environment and ask the user which to use. The prefix is permanent on every component created with it. See the solution skill's publisher discovery flow.

3. Use Documented Auth Patterns

Authentication is handled by pac auth create (for PAC CLI) and scripts/auth.py (for Python scripts and the SDK).

NEVER:

• Read or parse raw token cache files (e.g., tokencache_msalv3.dat)
• Implement your own MSAL device-code flow
• Hard-code tokens or credentials in scripts
• Invent a new auth mechanism

If auth is expired or missing, re-run pac auth create or check .env credentials. See the dv-connect skill.

4. Follow Skill Instructions, Don't Improvise

Each skill documents a specific, tested sequence of steps. Follow them. If a skill says "use the Python SDK," use the Python SDK — do not substitute a raw HTTP call, a different library, or a different language. If a skill says "run this command," run that command — do not invent an alternative.

If you hit a gap (something the skills don't cover), say so honestly and suggest a workaround. Do not hallucinate a path or improvise a solution using tools the skills don't mention.

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UX Principle: Natural Language First

Users should never need to invoke skills or slash commands directly. The intended workflow is:

1. Install the plugin
2. Describe what you want in plain English
3. Claude figures out the right sequence of tools, APIs, and scripts

Example prompt: "I want to create an extension called IronHandle for Dynamics CRM in this Git repo folder that adds a 'nickname' column to the account table and populates it with a clever nickname every time a new account is created."

From that single prompt, Claude should orchestrate the full sequence: check if the workspace is initialized → create metadata via Web API → write and deploy a C# plugin → pull the solution to the repo. No skill names, no commands — just intent.

Skills exist as Claude's knowledge, not as user-facing commands. Each skill documents how to do one thing well. Claude chains them together based on what the user describes. If a capability gap exists (e.g., prompt columns aren't programmatically creatable yet), say so honestly and suggest workarounds rather than hallucinating a path.

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Multi-Environment Rule (MANDATORY)

Pro-dev scenarios involve multiple environments (dev, test, staging, prod) and multiple sets of credentials. Never assume the active PAC auth profile, values in .env, or anything from memory or a previous session reflects the correct target for the current task.

Before the FIRST operation that touches a specific environment — creating a table, deploying a plugin, pushing a solution, inserting data — you MUST:

1. Show the user the environment URL you intend to use
2. Ask them to confirm it is correct
3. Run pac org who to verify the active connection matches

"I'm about to make changes to <URL>. Is this the correct target environment?"

Do not proceed until the user explicitly confirms. This is the single most important safety check in the plugin. Skipping it risks making irreversible changes to the wrong environment.

Once confirmed for a session, you do not need to re-confirm for every subsequent operation in the same session against the same environment.

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What This Plugin Covers

This plugin covers Dataverse / Power Platform development: solutions, tables, columns, forms, views, and data operations (CRUD, bulk, analytics).

It does not cover:

• Power Automate flows (use the maker portal or Power Automate Management API)
• Canvas apps (use pac canvas or the maker portal)
• Azure infrastructure beyond what's needed for service principal setup
• Business Central or other Dynamics products

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Tool Capabilities — Which Tool for Which Job

Understanding the real limits of each tool prevents hallucinated paths. This is the one piece of context no individual skill owns.

Tool	Use for	Does NOT support
MCP Server	Data CRUD (create/read/update/delete records), table create/update/delete/list/describe, column add via update_table, keyword search, single-record fetch	Forms, Views, Relationships, Option Sets, Solutions. Note: table creation may timeout but still succeed — always describe_table before retrying. Run queries sequentially (parallel calls timeout). Column names with spaces normalize to underscores (e.g., "Specialty Area" → cr9ac_specialty_area). SQL limitations: The read_query tool uses Dataverse SQL, which does NOT support: DISTINCT, HAVING, subqueries, OFFSET, UNION, CASE/IF, CAST/CONVERT, or date functions. For analytical queries that need these (e.g., finding duplicates, unmatched records, filtered aggregates), use Python with OData or pandas — see the python-sdk skill. Bulk operations: MCP create_record creates one record at a time. For 50+ records, use the Web API $batch endpoint or Python SDK CreateMultiple instead — see the python-sdk skill.
Python SDK	Preferred for all scripted data work and schema creation. Data CRUD, upsert (alternate keys), bulk create/update/upsert/delete (uses CreateMultiple/UpdateMultiple internally), OData queries (select/filter/expand/orderby/top), read-only SQL, table create/delete/metadata, add/remove columns, relationship metadata CRUD (1:N, N:N, lookup fields), alternate key management, file column uploads (chunked >128MB), context manager with connection pooling	Forms, Views, global Option Sets, record association ($ref), $apply aggregation, custom action invocation, generic $batch
Web API	Everything — forms, views, relationships, option sets, columns, table definitions, unbound actions, $ref association	Nothing (full MetadataService + OData access)
PAC CLI	Solution export/import/pack/unpack, environment create/list/delete/reset, auth profile management, plugin updates (pac plugin push — first-time registration requires Web API), user/role assignment (pac admin assign-user), solution component management	Data CRUD, metadata creation (tables/columns/forms)
Azure CLI	App registrations, service principals, credential management	Dataverse-specific operations
GitHub CLI	Repo management, GitHub secrets, Actions workflow status	Dataverse-specific operations

Tool priority (always follow this order): MCP (if available) for simple reads, queries, and ≤10 record CRUD → Python SDK for scripted data, bulk operations, schema creation, and analysis → Web API for operations the SDK doesn't cover (forms, views, option sets) → PAC CLI for solution lifecycle. MCP tools not in your tool list? → Load dv-connect to set them up (see below).

Volume guidance: MCP create_record is fine for 1–10 records. For 10+ records, use Python SDK client.records.create(table, list_of_dicts) — it uses CreateMultiple internally and handles batching. For data profiling and analytics beyond simple GROUP BY, use Python with pandas (see python-sdk skill). For aggregation queries ($apply), use the Web API directly.

Note: The Python SDK is in preview — breaking changes possible.

MCP Availability Check

If the user's request involves MCP — either explicitly ("connect via MCP", "use MCP", "query via MCP") or implicitly (conversational data queries where MCP would be the natural tool) — check whether Dataverse MCP tools are available in your current tool list (e.g., list_tables, describe_table, read_query, create_record).

If MCP tools are NOT available and the user explicitly asked for MCP:

1. Do NOT silently fall back to the Python SDK or Web API
2. Tell the user: "Dataverse MCP tools aren't configured in this session yet."
3. Load the dv-connect skill to set up the MCP server
4. After MCP is configured, stop here — the session must be restarted for MCP tools to appear (if running in Claude Code, remind them to resume the session correctly: "Remember to use claude --continue to resume the session without losing context"). Do not fall back to the SDK or proceed with other tools. Wait for the user to restart and come back.

If MCP tools are NOT available and the user asked a simple data question (e.g., "how many accounts with 'jeff'?"):

1. Answer the question using the Python SDK (don't block the user)
2. After answering, suggest: "This would be even simpler with MCP configured — want me to set that up?"

If MCP tools ARE available, prefer MCP for simple reads/queries/small CRUD. Use the SDK only when a script is needed.

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Available Skills

Each skill's frontmatter contains WHEN/DO NOT USE WHEN triggers that Claude uses for automatic routing. This index is for human reference only.

Skill	What it covers
dv-connect	Connect to Dataverse: install tools, authenticate, create .env, configure MCP, verify connection
dv-metadata	Create/modify tables, columns, relationships, forms, views via Web API
dv-python-sdk	Data CRUD, bulk ops, OData queries, file uploads, bulk import, data profiling, notebook analysis via Python SDK
dv-solution	Solution create/export/import/pack/unpack, post-import validation

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Scripts

The plugin ships utility scripts in scripts/:

Script	Purpose
auth.py	Azure Identity token/credential acquisition — used by all other scripts and the SDK
enable-mcp-client.py	Add the MCP Client ID to the list of allowed MCP clients in Dataverse

For data operations and post-import validation, use the Python SDK directly (inline in your own scripts). See the dv-python-sdk skill for SDK patterns and the dv-solution skill for validation queries.

Any Web API call that goes beyond a one-off query should be written as a Python script and committed to /scripts/. Use scripts/auth.py for token acquisition.

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Windows Scripting Rules

When running in Git Bash on Windows (the default for Claude Code on Windows):

• ASCII only in .py files. Curly quotes, em dashes, or other non-ASCII characters cause SyntaxError. Use straight quotes and regular dashes.
• No python -c for multiline code. Shell quoting differences between Git Bash and CMD break multiline python -c commands. Write a .py file instead.
• PAC CLI may need a PowerShell wrapper. If pac hangs or fails in Git Bash, use powershell -Command "& pac.cmd <args>". See the setup skill for details.
• Generate GUIDs in Python scripts, not via shell backtick-substitution: str(uuid.uuid4()) inside the .py file.

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Before Any Metadata Change: Confirm Solution

Before creating tables, columns, or other metadata, ensure a solution exists to contain the work:

1. Ask the user: "What solution should these components go into?"
2. If a solution name is in .env (SOLUTION_NAME), confirm it with the user
3. If no solution exists yet, create one (see the dv-solution skill)
4. Use the MSCRM.SolutionName header on all Web API metadata calls to auto-add components

Creating metadata without a solution means it exists only in the default solution and cannot be cleanly exported or deployed. Always solution-first.

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After Any Change: Pull to Repo (MANDATORY)

Any time you make a metadata change (via MCP, Web API, or the maker portal), you must end the session by pulling:

pac solution export --name <SOLUTION_NAME> --path ./solutions/<SOLUTION_NAME>.zip --managed false
pac solution unpack --zipfile ./solutions/<SOLUTION_NAME>.zip --folder ./solutions/<SOLUTION_NAME>
rm ./solutions/<SOLUTION_NAME>.zip
git add ./solutions/<SOLUTION_NAME>
git commit -m "feat: <description>"
git push

The repo is always the source of truth.