--- name: Dataverse Overview slug: dataverse-overview category: AI Engineering description: Dataverse Overview provides the cross-cutting context for Dataverse and Power Platform work, including scope, the specialist skill map, tool capabilities, safety rules, and the safe change lifecycle. Use it first when working on Dataverse, Dynamics 365, CRM, or ERP tasks. github: "https://github.com/microsoft/Dataverse-skills/tree/main/.github/plugins/dataverse/skills/dv-overview" stars: 216 forks: 57 install: "npx degit https://github.com/microsoft/Dataverse-skills/tree/main/.github/plugins/dataverse/skills/dv-overview ~/.claude/skills/dv-overview" installs_to: ~/.claude/skills/dv-overview source_path: .github/plugins/dataverse/skills/dv-overview/SKILL.md collection_size: 9 category_size: 2970 collection_url: "https://dirskills.com/collections/microsoft/Dataverse-skills" added: 2026-09-04T05:25:38.836Z last_synced: 2026-09-04T05:25:38.836Z canonical_url: "https://dirskills.com/skills/dataverse-overview" --- # Dataverse Overview Dataverse Overview provides the cross-cutting context for Dataverse and Power Platform work, including scope, the specialist skill map, tool capabilities, safety rules, and the safe change lifecycle. Use it first when working on Dataverse, Dynamics 365, CRM, or ERP tasks. **Install:** ```bash npx degit https://github.com/microsoft/Dataverse-skills/tree/main/.github/plugins/dataverse/skills/dv-overview ~/.claude/skills/dv-overview ``` ## README # Skill: Overview — What to Use and When Load this skill first for any Dataverse work — it holds the cross-cutting context every task needs: scope, the tool-capability reference, the hard rules, and the change lifecycle. It does **not** route; the agent auto-selects specialist skills via their own WHEN/DO NOT USE WHEN frontmatter triggers. Users describe what they want in plain English; the agent chains skills automatically and never asks the user to name a skill or command. --- ## What This Plugin Covers Dataverse / Power Platform work for **every persona** — builders and agent devs, data scientists, environment admins, and business users — delivered by specialist skills. The agent loads and routes to these automatically via their frontmatter triggers — you never invoke them by name. | Area | Skill | | --- | --- | | Connect, authenticate, configure MCP, verify the environment | `dv-connect` | | Schema — tables, columns, relationships, forms, views; inspect existing schema | `dv-metadata` | | Data writes — record CRUD, bulk create/update/upsert, CSV/FK-ordered import, sample data | `dv-data` | | Data reads & analytics — OData queries, QueryBuilder, FetchXML (aggregation + N:N joins), DataFrames | `dv-query` | | Solution ALM — create, export, import, pack/unpack, post-import validation | `dv-solution` | | Finance and Operations X++ — author, build, deploy, DB sync, verify | `erp-xpp` | | Environment administration — bulk delete, retention/archival, org & OrgDB settings, recycle bin | `dv-admin` | | Security & access — roles, users, application users, business units, self-elevation (PAC CLI) | `dv-security` | **Model-driven apps:** the building blocks (tables, forms, views) are covered by `dv-metadata`; composing the app shell itself — site map and navigation — is **not yet** a first-class skill. **Out of scope:** - **Canvas apps** — a different technology; use `pac canvas` or the maker portal - **Power Automate flows** — use the maker portal or the Power Automate Management API - **Azure infrastructure** beyond what's needed for service-principal setup - **Business Central** or other Dynamics products --- ## Hard Rules Safety rules (init, auth, env confirmation) are non-negotiable. Tool selection (Rules 1, 2, 4) is capability-based. ### 0. Check Init State First Before writing ANY code or creating ANY files, **actively search your callable tools for any tool whose name or description contains `dataverse`** (tools may be registered under environment-specific names like `mcp__dataverse___read_query`, not just generic names). **If any Dataverse MCP tool is found, use it directly — skip the init check and all setup.** MCP auth is host-managed and does not need `.env` or `scripts/auth.py`. Never declare MCP unavailable based solely on the initially displayed tool list. If no MCP tool is found, check for an existing CLI profile — it's the fastest path for data operations: ```bash dataverse auth who ``` If that shows an active profile with an environment URL, use CLI directly for data operations (see `dv-query`/`dv-data` examples) — no `.env`, `auth.py`, or workspace setup needed. For explicit "connect" or "set up" requests, run `dv-connect` regardless — it configures MCP, SDK, and PAC. If no CLI profile exists, check workspace init: ```bash ls .env scripts/auth.py 2>/dev/null ``` - If BOTH exist: proceed to the task. - If EITHER is missing: run `python /auth.py --ping`. If it prints `REACHABLE` (exit 0), the workspace is bootable without pip -- confirm the URL and proceed. If `--ping` fails, **run `dv-connect`**. ### 1. Python for scripting; the CLIs and MCP are first-class Python is the language for automation **logic** (transformation, control flow, retry, CSV). The toolchain (`scripts/auth.py`, the SDK, skill examples) is Python-based. But MCP tools, the Dataverse CLI (`dataverse`), the Python SDK, and the PAC CLI (`pac`) are all **first-class tool invocations** — use whichever fits. The Dataverse CLI has the same standing as `pac`, which is invoked freely across the solution and metadata skills. **NEVER:** - Write automation *logic* in JavaScript/TypeScript/Node.js (`npm`, `yarn`, `pnpm`, `package.json`, `node_modules/`) - Use `@azure/msal-node`, `@azure/identity`, or any Node.js Azure SDK - Implement a bespoke MSAL / device-code flow — auth is `scripts/auth.py`, `pac auth`, and the Dataverse CLI **ALWAYS:** - Use `pip install` and the Python SDK (`PowerPlatform-Dataverse-Client`) for data and schema logic - Use `scripts/auth.py` for tokens/credentials; `azure-identity` (Python) for Azure credential flows - Treat the Dataverse CLI (`dataverse`) and `pac` as allowed first-party CLIs ### 2. Pick the surface that fits — capability awareness, not a fixed order No mandated tool order. Each surface has a capability profile; pick what fits the job and the surface you are already in — soft defaults, not a required sequence. The full matrix is in **Tool Capabilities** below; the principles: - Prefer a managed surface (MCP, the Dataverse CLI, or the SDK) over hand-rolled raw OData — they carry auth, paging, retry, and geo routing that raw HTTP re-implements. When MCP can't handle it (bulk >25 records, large reads, advanced schema like forms/views/N:N relationships/global option sets/alternate keys, multi-step workflows, analytics, or MCP isn't available), the **Python SDK** is the default. - **Raw Web API is the last-resort escape hatch** for surfaces with no managed path (unbound actions like `PublishXml`, global option sets, anything without a first-class SDK/CLI command) — and even then prefer `dataverse api` (managed auth, exit codes) over hand-rolled `urllib`/`get_token`. Forms/views are **not** raw-only (SDK `records.create`/`update` on `systemform`/`savedquery`; only `PublishXml` needs `dataverse api`). Aggregation/N:N joins aren't raw-only either: `client.query.fetchxml()` (aggregates + link-entity), or the CLI's `data associate` for N:N writes. - If an SDK method fails or a PAC command seems missing, check the relevant skill before hand-rolling raw HTTP. **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. **SDK record payloads:** provide the correct SchemaName casing on `@odata.bind` keys (e.g., `new_AccountId@odata.bind`); the SDK does not auto-correct wrong casing. **Raw Web API calls** (forms, views, metadata): casing is entirely manual — a lowercase `new_accountid@odata.bind` will 400. **Publisher prefix:** Never hardcode a prefix (especially `new`); query existing publishers and ask the user. The prefix is permanent. See the solution skill's publisher discovery flow. ### 3. Use Documented Auth Patterns Three entry points, one shared sign-in: - **`dataverse auth create`** (Dataverse CLI) writes a shared MSAL token cache. That sign-in serves CLI, MCP proxy, **and** `scripts/auth.py` via `msal-extensions`. - **`scripts/auth.py`** is the Python/SDK auth entry point. Order: service principal → shared CLI cache → device-code. Use `get_client(skill)` (SDK) or `get_plugin_headers(skill, get_token())` (raw Web API) — both stamp attribution. - **`pac auth create`** (PAC CLI) authenticates `pac` for `dv-solution`, `dv-admin`, and `erp-xpp`. **Telemetry attribution (keep it deterministic):** every request carries a closed-schema `app=dataverse-skills/;skill=;agent=` context so the server sees which skill routed each OData call. It is baked in — `get_client(skill)` and `get_plugin_headers(skill, ...)` stamp it on the SDK and raw-HTTP paths; the Dataverse CLI auto-stamps `DataverseCli/` + the command, and you add the skill with `--context "app=dataverse-skills/;skill=;agent="` (the CLI wraps it in parentheses itself — do not pre-wrap). Never modify, omit, or free-form this context — it is a closed schema (allowlisted skill/agent, no PII). **NEVER:** - Read or parse raw token cache files (e.g., `tokencache_msalv3.dat`) — reuse the cache only through `scripts/auth.py` / `msal-extensions` - 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 `dataverse auth create` (or `pac auth create`), or check `.env`. See the `dv-connect` skill. ### 4. Be honest about gaps — don't hallucinate Each skill documents a tested sequence — follow it when it fits. The skills are the source of truth for the supported, non-deprecated API. If a call fails with `AttributeError`, the installed SDK version may not have it — check the skill's version note and use the documented alternative. **The honesty guard:** if you hit a gap the skills don't cover, say so and suggest a workaround. **Do not hallucinate an unsupported path** — do not invent a method, parameter, or endpoint that isn't documented. If unsure, say so. **Connectivity is not auth.** A `login.microsoftonline.com` token can succeed while the org's data-plane domain is unreachable (restricted-egress hosts like ChatGPT Work Mode). Never report a count or result you didn't get from a real call that returned — verify with `python scripts/auth.py --check`; if it fails, say "unreachable," never a fabricated number. On a constrained host, lead with the SDK, not CLI/MCP. See `dv-connect/references/headless-hosts.md`. --- ## 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, batch up to 25 per call), table create/update/delete + column add (incl. local choice/multiselect + lookup/customer), schema + record inspection via `describe`, metadata search (`search`), data + file-content search (`search_data`, when Dataverse search is enabled), file upload/download | Forms, Views, **global** Option Sets, **N:N** relationships, alternate keys, Solutions (lookup + local choice/multiselect columns **are** supported via `create_table`/`update_table`). **Note:** table creation may timeout but still succeed — always `describe` (e.g. `describe('tables/{name}')`) before retrying. Run queries sequentially (parallel calls timeout). Column names with spaces normalize to underscores (e.g., `"Specialty Area"` → `cr9ac_specialty_area`). **SQL (`read_query`):** supports `JOIN`, `GROUP BY` (COUNT/SUM/AVG/MIN/MAX), `TOP`, `WHERE`, `ORDER BY`; does NOT support `DISTINCT`, `HAVING`, subqueries, `OFFSET`, `UNION`, `CASE`/`IF`, `CAST`/`CONVERT`, CTE, or date functions. For those, use `client.query.sql()` (also allows `DISTINCT`, <5K rows), `$apply`, or a builder->DataFrame with pandas — see `dv-query`. **Bulk:** MCP `create_record`/`update_record`/`delete_record` batch up to 25 records per call; for larger bulk use the SDK `CreateMultiple` — see `dv-data`. | | **Python SDK (`dv-data`)** | Scripted data writes, especially at volume. Record CRUD, upsert (alternate keys), bulk create/update/upsert (CreateMultiple/UpdateMultiple/UpsertMultiple), CSV import with lookup resolution, file column uploads (chunked >128MB) | global Option Sets, record association (`$ref`), `$apply` aggregation, table/column/relationship creation (use `dv-metadata`), custom action invocation | | **Python SDK (`dv-query`)** | Bulk reads and analytics. Multi-page record iteration, OData queries (select/filter/expand/orderby), QueryBuilder fluent API, GUID-free display (formatted values), `$expand` to resolve lookups, **aggregation and N:N joins via `client.query.fetchxml()`** (aggregate FetchXML + link-entity), pandas DataFrame handoff (`client.query.builder(...).execute().to_dataframe()`) for exports, Jupyter notebook snippets | OData `$apply` and N:N `$expand` on the **QueryBuilder** path — use `records.list(expand=...)` for N:N, or `fetchxml()` for aggregates (not raw `urllib`) | | **Dataverse CLI (`dataverse`)** | Headless data plane: `data` CRUD, `associate`/`disassociate` (N:N + `$ref`), `data upload`; `api request`/`invoke` (Web API escape hatch); `api list`/`describe` (Custom API discovery) | Metadata/schema (use SDK — `dv-metadata`), solution ALM (use PAC), forms/views; **blocked on ChatGPT web / Codex cloud** (no .NET runtime) — use the SDK | | **PAC CLI** | Solution export/import/pack/unpack, ERP X++ SDK install + package scaffold/compile/deploy/DB sync, 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`), add solution components (`pac solution add-solution-component`) | Data CRUD, Dataverse metadata creation (tables/columns/forms), listing solution components (no `list-components` — query `solutioncomponent` via SDK/CLI); X++ SDK install/compile require Windows | | **Azure CLI** | App registrations, service principals, credential management | Dataverse-specific operations | | **GitHub CLI** | Repo management, GitHub secrets, Actions workflow status | Dataverse-specific operations | | **Raw Web API** (last resort) | Only when **no** managed surface exposes the operation — i.e. not doable via MCP, the Python SDK, the Dataverse CLI, or the `dataverse api` escape hatch. Genuine cases: unbound actions like `PublishXml`, global option sets, and similar edge cases (**not** forms/views — those are SDK record CRUD on `systemform`/`savedquery`). Even then, prefer `dataverse api` (managed auth + skill attribution) over hand-rolled `urllib`. | Functionally nothing (full OData/MetadataService) — but raw `urllib` bypasses managed auth, paging, retry, and skill attribution, so treat it as the path of last resort | **Routing:** the table shows what each surface does; the *how to choose* principle (soft defaults, not a fixed order) is Hard Rule 2. MCP tools not in your list? Load `dv-connect`. **Volume guidance:** CLI `dataverse data create/query/count` for one-off commands; MCP for up to ~25 records per call or simple filters; the SDK's `CreateMultiple` for larger bulk writes (chunk large sets starting ~1,000 — see `dv-data`) and `dv-query` for bulk reads; Web API for `$apply` aggregation. **SDK method cheat-sheet** (anti-hallucination, *not* a preference signal): SDK method names are the least discoverable surface, so agents invent them. This maps common ops to the exact call. Each op is equally reachable via MCP/CLI per Hard Rule 2; see the noted skill for the full pattern. | Operation | SDK call | Skill | | --- | --- | --- | | Create / update / delete records | `client.records.create()` / `.update()` / `.delete()` (pass a list for bulk) | `dv-data` | | Upsert on an alternate key | `client.records.upsert()` | `dv-data` | | Query / filter records | `client.records.list(...)` (flat) or `.list_pages(...)` (streaming) | `dv-query` | | One record by GUID | `client.records.retrieve(table, guid)` (`None` if missing) | `dv-query` | | Aggregation / server-side joins | `client.query.fetchxml(xml)` (aggregates + link-entity) | `dv-query` | | Fluent query build (chainable) | `client.query.builder(Table).where(...).execute()` | `dv-query` | | Limited SQL read | `client.query.sql("SELECT ...")` | `dv-query` | | Load into pandas | `client.query.builder(table).select(...).execute().to_dataframe()` | `dv-query` | | Upload to a file column | `client.files.upload(...)` | `dv-data` | | Create tables / columns / lookups / N:N | `client.tables.create()` / `.add_columns()` / `.create_lookup_field()` / `.create_many_to_many_relationship()` | `dv-metadata` | | Create an alternate key (enables upsert) | `client.tables.create_alternate_key(...)` | `dv-metadata` | | Inspect existing schema | `client.tables.list_columns(table)` / `.list_table_relationships(table)` | `dv-metadata` | | Create publisher / solution | `client.records.create("publisher" / "solution", {...})` | `dv-solution` | ### MCP Availability Check If the user's request involves MCP — explicitly or implicitly — search your callable tools for any tool whose name or description contains `dataverse` (same search as Hard Rule 0). **If MCP NOT available and user explicitly asked for MCP** ("use MCP to query"): 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 `dv-connect` to set up the MCP server 4. After MCP is configured, **stop** — the session must restart for MCP tools to appear. Do not proceed with SDK. **If MCP NOT available and user asked a data question** ("how many accounts?"): 1. Use the CLI (if profile exists) or SDK to answer. Do not block the user. 2. After answering, offer: "MCP would handle this conversationally — want me to set it up?" The distinction matters: explicit MCP request → block and set up MCP; implicit question → answer with SDK, offer MCP setup. **If MCP tools ARE available**, prefer MCP for simple reads/queries/small CRUD. Use the SDK only when a script is needed. --- ## The Change Lifecycle — Operate Safely For any real change, walk these three steps in order: confirm **where**, confirm the **container**, then persist the **result**. ### Step 1 — Confirm the Environment (MANDATORY) Dataverse work often spans 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 ``. 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. ### Step 2 — Confirm the Solution (before any metadata change) 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, **load the `dv-solution` skill** and follow its publisher discovery + solution creation flow. Use the SDK — **never raw Web API** — to create publisher and solution records: ```python # Quick reference — full pattern with publisher discovery is in dv-solution publisher_id = client.records.create("publisher", { "uniquename": "", "friendlyname": "", "customizationprefix": "", "description": "", }) solution_id = client.records.create("solution", { "uniquename": "", "friendlyname": "", "version": "1.0.0.0", "publisherid@odata.bind": f"/publishers({publisher_id})", }) ``` 4. Pass `solution=""` on all SDK calls, or include `"MSCRM.SolutionName": ""` header on raw Web API metadata calls. Creating metadata without a solution means it exists only in the default solution and cannot be cleanly exported or deployed. Always solution-first. ### Step 3 — 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: ```bash pac solution export --name --path ./solutions/.zip --managed false pac solution unpack --zipfile ./solutions/