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Import Notebook to Fabric Workspace

import_notebook_to_fabric

Upload local Jupyter notebooks to Microsoft Fabric workspaces for data analysis and engineering workflows. Transfer .ipynb files with optional folder organization and descriptions.

Instructions

Upload a local .ipynb into a Fabric workspace identified by name.

Imports a Jupyter notebook from the local filesystem into a Microsoft Fabric workspace. The notebook file must be in .ipynb format. The notebook can be organized into folders using forward slashes in the display name (e.g., "demos/hello_world").

Parameters: workspace_name: The display name of the target workspace (case-sensitive as shown in Fabric). notebook_display_name: Desired name (optionally with folders, e.g. "demos/hello_world") inside Fabric. local_notebook_path: Path to the notebook file (absolute or repo-relative). description: Optional description for the notebook.

Returns: Dictionary with status, message, and artifact_id if successful.

Example: python result = import_notebook_to_fabric( workspace_name="My Workspace", notebook_display_name="analysis/customer_analysis", local_notebook_path="notebooks/customer_analysis.ipynb", description="Customer behavior analysis notebook" )

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
workspace_nameYes
notebook_display_nameYes
local_notebook_pathYes
descriptionNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior3/5

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

With no annotations provided, the description carries full burden. It discloses the upload action and format requirements, but doesn't mention authentication needs, rate limits, error handling, or what happens if the workspace doesn't exist. It states the return format but lacks details about failure modes or side effects.

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?

The description is well-structured with clear sections: purpose statement, parameter explanations, return format, and example. While comprehensive, some sentences could be more concise (e.g., 'The notebook file must be in .ipynb format' could be merged with the first sentence).

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?

Given 4 parameters with 0% schema coverage and no annotations, the description does an excellent job explaining parameter semantics and providing an example. The output schema exists, so return values don't need explanation. However, for a mutation tool with no annotations, it could better address behavioral aspects like error conditions or side effects.

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 fully compensate. It provides clear explanations for all 4 parameters: workspace_name (target workspace, case-sensitive), notebook_display_name (desired name with folder structure), local_notebook_path (file path), and description (optional). The example further clarifies usage with concrete values.

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 clearly states the specific action ('Upload a local .ipynb'), target resource ('into a Microsoft Fabric workspace'), and format constraints ('.ipynb format'). It distinguishes itself from sibling tools by focusing on notebook import rather than pipeline activities, semantic model operations, or workspace/item listing.

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

Usage Guidelines3/5

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

The description implies usage context (uploading local notebooks to Fabric) but doesn't explicitly state when to use this tool versus alternatives like 'get_notebook_content' or 'list_items'. It mentions format requirements but doesn't provide guidance on prerequisites or error conditions.

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

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