powerbi-orchestrator-mcp

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deploy_to_workspace

Publishes a Power BI .pbip project to a Fabric/Power BI workspace after pre-deploy quality gates, with optional scheduled daily refresh.

Instructions

Deploy a PBIP project to a Fabric/Power BI workspace with pre-deploy gates.

Use this tool when the user asks to:

Deploy, publish, or release a Power BI project (.pbip) to Microsoft Fabric or Power BI Service.
Run pre-deployment quality gates before publishing.
Schedule automatic daily dataset refreshes upon publication.

Args: pbip_path: Local filesystem path to the root .pbip directory. workspace_id: Target Fabric / Power BI workspace ID (UUID). refresh_daily_hour: Daily UTC hour (0-23) for scheduled refresh (default: 6 AM UTC). findings_json: Optional list or JSON string of pre-existing audit findings to evaluate. gate_profile: Quality gate profile ("strict", "standard", "lenient"). auth_mode: "interactive" (browser login) or "service_principal". When omitted, PBI_AUTH_MODE is used, falling back to "interactive". tenant_id: Azure AD tenant ID. client_id: Azure AD client ID. dry_run: When True (default) evaluates the gate and reports the planned operations WITHOUT calling any Fabric API. Pass dry_run=False to actually publish. mock: If True, simulate deployment without calling external APIs.

Returns: Dict with deployment status, gate evaluation results, published item IDs, and refresh configuration.

Input Schema

Name	Required	Default
`mock`	No
`dry_run`	No
`auth_mode`	No
`client_id`	No
`pbip_path`	Yes
`tenant_id`	No
`gate_profile`	No	standard
`workspace_id`	Yes
`findings_json`	No	[]
`refresh_daily_hour`	No

Output Schema

Name	Required	Description	Default
No arguments

Schema Changelog

Changes observed during successful MCP inspections.

Changed3 schema fields changedv1.23.0Oct 23, 2026
- addedInput schema / properties / auth_mode / anyOf
```
Added value: +[
+  {
+    "type": "string"
+  },
+  {
+    "type": "null"
+  }
+]
```
- changedInput schema / properties / auth_mode / default
```
Previous value: -"interactive"New value: +null
```
- removedInput schema / properties / auth_mode / type
```
Removed value: -"string"
```

Changed2 schema fields changedv1.18.0Oct 17, 2026

removedInput schema / properties / client_secret

Removed value: -{
-  "anyOf": [
-    {
-      "type": "string"
-    },
-    {
-      "type": "null"
-    }
-  ],
-  "default": null,
-  "title": "Client Secret"
-}

addedInput schema / properties / dry_run

Added value: +{
+  "default": true,
+  "title": "Dry Run",
+  "type": "boolean"
+}

Changed1 schema field changedv1.11.0Oct 2, 2026
- removedInput schema / properties / findings_json / type
```
Removed value: -"string"
```
First observedv0.1.0Sep 10, 2026

TDQS

A4.6/5.0

Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description carries the burden and does well: it explains that dry_run=True evaluates gates and reports planned operations without calling Fabric APIs, dry_run=False actually publishes, and mock=True simulates deployment. It also documents auth fallback to PBI_AUTH_MODE and return contents, though it does not state required permissions or irreversible side effects of publishing.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is front-loaded with purpose and usage triggers, then uses a clean Args list and Returns summary. Every sentence serves a purpose, and the critical dry_run default is called out clearly without excess prose.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a 10-parameter mutation tool with no annotations and an output schema, the description is complete enough: it covers when to use it, what each parameter means, the safety-relevant dry_run/mock behavior, authentication options, and what the result contains. An agent can invoke this correctly without additional guidance.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, so the description must compensate, and it does: every parameter is explained in Args, including default behaviors (dry_run=True, refresh_daily_hour=6), accepted gate profiles, auth mode fallback, and the dual list/JSON-string nature of findings_json. This adds substantial meaning beyond bare schema types.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description opens with a specific verb and resource: deploying a PBIP project to a Fabric/Power BI workspace with pre-deploy gates. It clearly distinguishes this from generic publishing or refresh tools and names the artifact type (.pbip), target platform, and gate behavior.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

It provides explicit triggers: deploy/publish/release a .pbip project, run pre-deployment quality gates before publishing, and schedule daily refresh. However, it does not distinguish this from the sibling pre_deploy_check tool or say when to use this versus that or sync_git_to_workspace, leaving a small ambiguity.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.