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commit_workspace_to_git

Back up and version-control a Fabric workspace by exporting reports and semantic models to local PBIP files and committing them to Git.

Instructions

Export and commit a Fabric workspace into a local Git repository.

Use this tool when the user asks to:

  • Back up or version-control a Fabric workspace into Git.

  • Snapshot reports and semantic models into local PBIP files with Git commits.

Args: workspace_id: Source Fabric workspace ID (UUID). output_repo_path: Local path to destination Git repository. branch: Git branch to commit into. commit_message: Commit message describing the snapshot. exclude_items: Optional list of item IDs to exclude. dry_run: If True, inspect items without creating git commits.

Returns: Dict with committed items, commit SHA, and repository status.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
branchNo
dry_runNo
workspace_idYes
exclude_itemsNo
commit_messageNo
output_repo_pathYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A4.1/5.0
Behavior3/5

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

With no annotations, the description carries the full burden. It usefully discloses that dry_run defaults to True and returns committed items, commit SHA, and repo status, but says nothing about Git authentication/credentials, whether existing files in the target repo are overwritten or staged, or whether the operation is reversible.

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

Conciseness4/5

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

Well front-loaded: purpose sentence first, then trigger bullets, then Args, then Returns. The trigger bullets partially restate the opening sentence, which is slight redundancy, but nothing is padded or wasted.

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

Completeness4/5

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

For a six-parameter mutation tool with an output schema already present, the description covers purpose, triggers, parameters, and a return summary. The main remaining gap is operational prerequisites (Git auth/permissions and repo state preconditions), which an agent would want before committing.

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

Parameters4/5

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

Schema description coverage is 0%, so the Args block is the only parameter documentation and it covers all six parameters, including the important dry_run semantic ('inspect items without creating git commits'). It remains thin on defaults (branch, commit_message) and on what 'item IDs' in exclude_items actually refer to.

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 names a specific verb pair (export and commit), the resource (Fabric workspace), and the destination (local Git repository). The 'into a local Git repository' direction cleanly separates it from the sibling sync_git_to_workspace, which flows the other way.

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 gives an explicit 'Use this tool when the user asks to' block with two concrete trigger scenarios (backup/version-control a workspace, snapshot reports and semantic models into PBIP). It stops short of naming when NOT to use it or pointing to the reverse-direction sibling, so it is clear context without exclusions.

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