dv-connect

Skill: Connect

One-step connection to Dataverse. Handles tool installation, authentication, environment selection, workspace initialization, MCP configuration, and verification — all idempotently. Each step checks if it's already done and skips if so.

Environment-First Rule — All metadata (solutions, columns, tables, forms, views) and plugin registrations are created in the Dynamics environment via API or scripts, then pulled into the repo. Never write or edit solution XML by hand to create new components.

Execute every step in order. Do not skip ahead, even if a later step appears more relevant to the user's immediate goal. Exception: Step 0 below can short-circuit the entire flow if the workspace is already set up.

---

Step 0: Detect existing setup (run this first)

Before touching anything, check whether this workspace is already connected to a Dataverse environment. This matters a lot on claude --continue or any re-run — the whole flow takes several minutes, and repeating it on an already-configured workspace overwrites .env, re-registers MCP, and wastes time.

Run these checks in order. If all four pass, skip straight to Step 7 (final verification) and stop there.

1. .env is present and complete — file exists at the workspace root and contains non-empty values for DATAVERSE_URL, TENANT_ID, and MCP_CLIENT_ID
2. MCP is registered — .mcp.json (Claude Code) or the equivalent Copilot config file has a dataverse-* server entry pointing at the DATAVERSE_URL from .env
3. PAC CLI auth matches .env — pac auth list shows a profile whose Environment Url matches DATAVERSE_URL, and pac org who against that profile succeeds
4. Python SDK is importable and current — python -c "from PowerPlatform.Dataverse.client import DataverseClient; import pandas; from importlib.metadata import version; v=version('PowerPlatform-Dataverse-Client'); assert v>='0.1.0b9', f'SDK {v} is outdated, need >=0.1.0b9'" exits 0

If all pass: Tell the user you detected an existing setup, list what you found (URL, profile name, MCP server name), then jump to Step 7. Do not rewrite .env, do not re-register MCP, do not re-run pip install.

Example: "Detected existing Dataverse setup at {DATAVERSE_URL} (auth profile: {PROFILE}, MCP server: dataverse-{orgid}). Running verification only."

If any check fails: Proceed through the normal flow (Steps 1–7), but still use each step's own skip condition. A partially-configured workspace doesn't need a full redo — e.g., if only .env and MCP are missing but tools and auth are fine, start at Step 2 or Step 3.

---

Step 1: Ensure tools are installed

Check each tool independently — do not use fail-fast parallel execution. If one tool check fails, continue checking the others so you can report all missing tools at once. See tools-setup.md for installation commands and platform-specific notes.

Tool	Check
Python 3	python --version
Git	git --version
Node.js	node --version
PAC CLI	pac (prints version banner; note: pac --version is not a valid command and returns a non-zero exit code) (see tools-setup.md for Windows path discovery if not in PATH)
Dataverse CLI	npm list -g @microsoft/dataverse (prints @microsoft/dataverse@<version> if installed globally; prints (empty) if not)
.NET SDK	dotnet --version
Azure CLI	az --version

.NET SDK is needed for PAC CLI but NOT for the Dataverse CLI (the npm package bundles its own runtime). Node.js powers the Dataverse CLI npm package (@microsoft/dataverse), which is used as the MCP proxy and for scripted data plane actions. Azure CLI is used as a fallback for environment discovery when PAC CLI isn't available (see mcp-configuration.md Step 3b). GitHub CLI is not needed for connecting — it's used later for ALM/CI/CD scenarios (see dv-solution).

If any tool is missing, install it (see tools-setup.md), then verify. If winget installs a tool but it's not in PATH, ask the user to restart the terminal.

After Python is confirmed:

pip install --upgrade azure-identity requests PowerPlatform-Dataverse-Client pandas

After Node.js is confirmed, install or upgrade the Dataverse CLI to the latest version. This mirrors the pip install --upgrade pattern used for the Python SDK — running it on each connect ensures the CLI stays current:

npm install -g @microsoft/dataverse@latest

Skip condition: All tools present, Python SDK installed, and pandas importable (python -c "import pandas").

---

Step 2: Discover and select the environment

Before asking the user for a URL, check what's already available:

pac auth list
pac org who

If PAC CLI is authenticated:

• Show the currently active environment
• Offer to use it, switch to another (pac env list), or create a new one

If PAC CLI is not authenticated:

• Ask: "Do you want to connect to an existing environment or create a new one?"

Before selecting, check for tenant/region mismatch. If the target environment URL uses a different region (e.g., crm10.dynamics.com = APAC) than the currently authenticated account's environments, the current auth profile likely belongs to a different tenant. In that case, create a new auth profile for the correct tenant rather than trying pac org select (which will fail with "no organization found"):

pac auth create --name <profile-name> --environment <url>

To select from existing profiles:

pac auth select --name <profile-name>

To create a new environment (requires admin permissions):

pac admin create --name "<name>" --type "<type>" --region "<region>"

If this fails with permissions error, guide the user to Power Platform Admin Center to create it, then connect.

Confirm connection:

pac org who

Parse the output to extract DATAVERSE_URL and TENANT_ID.

If pac org who does not show a tenant ID, fall back to:

curl -sI https://<org>.crm.dynamics.com/api/data/v9.2/ \
  | grep -i "WWW-Authenticate" \
  | sed -n 's|.*login\.microsoftonline\.com/\([^/]*\).*|\1|p'

Skip condition: .env exists with valid DATAVERSE_URL and TENANT_ID, and pac org who confirms the connection.

---

Step 3: Create .env

Present authentication options:

How would you like to authenticate with Dataverse?

1. Interactive login (recommended) — Sign in via browser. No app registration needed. Token stays cached across sessions.
2. Service principal (for CI/CD) — Uses CLIENT_ID and CLIENT_SECRET from an Azure app registration.

Write .env directly — do not instruct the user to create it:

Detect the current tool (Claude or Copilot) from context and set MCP_CLIENT_ID automatically:

• Claude (CLI or VSCode extension): 0c412cc3-0dd6-449b-987f-05b053db9457
• GitHub Copilot: aebc6443-996d-45c2-90f0-388ff96faa56

Also set plugin attribution variables for User-Agent tagging. Fill in the two literals below from your own context — you (the agent) loaded this plugin, so you already know both values:

• PLUGIN_VERSION — the version field of your loaded plugin manifest (e.g. "1.5.0"). At runtime, auth.py re-reads this from the live manifest via host env vars; this .env entry is a fallback for offline cases.
• AGENT — your host identity, one of: claude-code, copilot, cursor, codex, or unknown. Must match an entry in _ALLOWED_AGENTS in auth.py — if you don't recognize your host, use unknown.

# Substitute these two literals from your loaded plugin context.
# Do NOT leave the angle-bracket placeholders — replace with real values.
plugin_version = "<plugin manifest version, e.g. 1.5.0>"
agent_host = "<your host name: claude-code | copilot | cursor | codex | unknown>"

with open(".env", "w") as f:
    f.write(f"DATAVERSE_URL={dataverse_url}\n")
    f.write(f"TENANT_ID={tenant_id}\n")
    f.write(f"MCP_CLIENT_ID={mcp_client_id}\n")
    f.write(f"DATAVERSE_PLUGIN_VERSION={plugin_version}\n")
    f.write(f"DATAVERSE_PLUGIN_AGENT={agent_host}\n")
    f.write(f"SOLUTION_NAME={solution_name}\n")
    f.write(f"PUBLISHER_PREFIX=\n")  # filled in when solution is created
    f.write(f"PAC_AUTH_PROFILE=nonprod\n")
    if client_id:
        f.write(f"CLIENT_ID={client_id}\n")
    if client_secret:
        f.write(f"CLIENT_SECRET={client_secret}\n")

Multi-environment repos: If the team deploys to multiple environments from the same repo, each developer's .env represents their current target. Consider .env.dev, .env.staging, etc., with a pattern like cp .env.dev .env to switch targets. Each developer manages their own local .env.

Ensure .env is in .gitignore:

import os

GITIGNORE_ENTRIES = [
    ".env", ".vscode/settings.json", ".claude/mcp_settings.json",
    ".token_cache.bin", "*.snk", "__pycache__/", "*.pyc",
    "solutions/*.zip", "plugins/**/bin/", "plugins/**/obj/",
]
gitignore = open(".gitignore").read() if os.path.exists(".gitignore") else ""
missing = [e for e in GITIGNORE_ENTRIES if e not in gitignore]
if missing:
    with open(".gitignore", "a") as f:
        f.write("\n" + "\n".join(missing) + "\n")

Skip condition: .env already exists with all required values.

---

Step 4: Set up project structure (new projects only)

If this is a new project (no scripts/ directory):

mkdir -p solutions plugins scripts

Copy plugin scripts:

cp .github/plugins/dataverse/scripts/auth.py scripts/

Copy templates/CLAUDE.md to the repo root if it doesn't exist. Replace placeholders ({{DATAVERSE_URL}}, {{SOLUTION_NAME}}, {{PUBLISHER_PREFIX}}) with values from .env.

Skip condition: scripts/auth.py exists.

---

Step 5: Verify the connection

pac org who
python scripts/auth.py

Both must succeed. Confirm the environment URL matches the intended target.

If either fails:

• pac org who fails → re-run Step 2
• python scripts/auth.py fails → check Python SDK install, check .env values

---

Step 6: Configure MCP server

Skip this step if MCP is already configured:

• .mcp.json or ~/.copilot/mcp-config.json contains a Dataverse server entry
• claude mcp list shows a dataverse-* server registered

If MCP is not configured, follow mcp-configuration.md:

1. Detect which tool the user is running (Copilot or Claude) from context
2. Set MCP_CLIENT_ID based on tool choice
3. Get environment URL from .env
4. Default to GA endpoint (/api/mcp)
5. Register the MCP server (Copilot: write JSON config; Claude: run claude mcp add command)
6. Handle admin consent and environment allowlist (one-time per tenant/environment)

Plugin attribution for MCP: This plugin uses the stdio proxy transport (npx @microsoft/dataverse mcp <url>) — the CLI runs as a local subprocess and proxies requests to the Dataverse MCP HTTP endpoint. When registering the stdio proxy, include DATAVERSE_OPERATION_CONTEXT in the env block so the CLI reads it at startup and appends it to its User-Agent on all outbound HTTP requests to /api/mcp. Build the value from .env:

DATAVERSE_OPERATION_CONTEXT=app=dataverse-skills/{DATAVERSE_PLUGIN_VERSION};skill=unknown;agent={DATAVERSE_PLUGIN_AGENT}

For Claude Code (claude mcp add -t stdio), pass it via -e DATAVERSE_OPERATION_CONTEXT=.... For Copilot/Cursor JSON configs, add it to the "env" object in the stdio server entry.

Important: MCP configuration requires an editor/CLI restart.

For Copilot: Write the JSON config, then:

✅ Dataverse MCP server configured. Restart your editor for changes to take effect.

For Claude: Run the claude mcp add command, then warn the user about the auth popup that will appear on next launch:

✅ Dataverse MCP server registered. Restart Claude Code to enable MCP tools. Remember to use claude --continue to resume the session without losing context.

On restart, a browser window will open asking you to sign in to your Dataverse environment. This is the MCP proxy authenticating on your behalf — sign in with the same account you used for PAC CLI (e.g., {username}). This only happens once; the token is cached for future sessions.

---

Step 7: Final verification

After the editor/CLI restarts, both of these must succeed before declaring the setup complete:

Check 1: claude mcp list (or Copilot equivalent) shows ✓ Connected

claude mcp list

This proves the MCP server process starts and speaks the MCP protocol. It does NOT by itself prove that data operations work — authentication, environment allowlisting, and endpoint reachability are only exercised on the first real tool call.

Check 2: Agent successfully calls list_tables and returns data

"List the tables in my Dataverse environment."

This proves end-to-end wiring: auth, tenant consent, environment allowlist, and endpoint reachability are all correct. If the agent falls back to PAC CLI or Web API, see mcp-configuration.md troubleshooting.

Only when both checks pass is the setup verified.

Interpreting failures:

• If Check 1 fails (server not ✓ Connected): the MCP server itself cannot start. Re-run Step 6 and check that npx/Node.js are installed and the MCP registration succeeded.
• If Check 1 passes but Check 2 fails (server starts but list_tables errors): the server can speak MCP but cannot reach or read Dataverse. Run --validate below to diagnose.

Diagnostic — --validate (for failure investigation only):

npx @microsoft/dataverse mcp {DATAVERSE_URL} --validate

This exercises two Dataverse MCP endpoints with a fresh authentication handshake and reports detailed errors (auth, allowlist, consent, endpoint reachability):

• GA / Production endpoint — {DATAVERSE_URL}/api/mcp. This is the one the plugin actually uses at runtime.
• Preview endpoint — {DATAVERSE_URL}/api/mcp_preview. Opt-in per environment; not used by the plugin.

Do not use --validate as a success gate on first-time setup. On a freshly configured workspace, the token cache hasn't warmed up, so --validate can fail with MsalClientException or 403 while MCP is actually working fine on subsequent real calls. Reserve --validate for diagnosing a confirmed failure in Check 1 or Check 2.

How to read --validate output:

• Look at the GA / Production endpoint (/api/mcp) result first. If this passes, MCP will work for normal plugin usage regardless of what the Preview endpoint reports.
• A 403 Forbidden on the Preview endpoint (/api/mcp_preview) is expected for most environments. Preview is opt-in per environment; if your environment hasn't enabled it, the Preview check will always fail. This does not indicate a broken setup.
• Ignore the overall exit code and the ⚠ Partial success warning in this case. The validator returns exit code 1 (failure) unless BOTH /api/mcp and /api/mcp_preview pass. Because most environments don't enable the Preview endpoint, --validate will exit 1 even when MCP is fully functional via the GA endpoint. Focus on per-endpoint results, not the aggregate status.
• If the GA endpoint (/api/mcp) fails: that's the real signal to investigate — auth, tenant consent, environment allowlist, or endpoint reachability.

MCP Server Capabilities

Task	Use
Create/read/update/delete data records	MCP server
Create a new table	MCP server
Explore what tables/columns exist	MCP server (list_tables, describe_table)
Add a column to an existing table	MCP server (update_table) for basic columns; SDK or Web API (see dv-metadata) for advanced options (choice columns, lookups, relationships)
Create a relationship / lookup	SDK (see dv-metadata)
Create or modify a form	Web API (see dv-metadata)
Create or modify a view	Web API (see dv-metadata)

After verifying MCP works, tell the user:

✅ Connected to Dataverse at {DATAVERSE_URL}. Tools installed, authenticated, MCP live.

You can now:

• Create tables, columns, and relationships (dv-metadata)
• Write and import data (dv-data)
• Query and analyze data (dv-query)
• Export and promote solutions (dv-solution)

To create your first solution, see the dv-solution skill. To load sample data (accounts, contacts, opportunities), ask: "Load demo data into my Dataverse environment."

---

Supported Agents

This plugin's skill files are natively loaded by both GitHub Copilot CLI and Claude Code CLI when installed as a plugin. No manual context-loading is needed — both agents discover and invoke skills automatically.

The PAC CLI commands, Python scripts, and XML templates work identically in both environments.