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bablulawrence

ms-fabric-mcp-server

by bablulawrence
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Python
Remote

Create Blank Pipeline

create_blank_pipeline

Initialize an empty data pipeline in Microsoft Fabric to prepare for adding activities like data copying and transformation workflows.

Instructions

Create a blank Fabric pipeline with no activities.

Creates a Data Pipeline in the specified workspace with an empty activities array, ready to be populated with activities later using the add_copy_activity_to_pipeline tool.

Parameters: workspace_name: The display name of the workspace where the pipeline will be created. pipeline_name: Name for the new pipeline (must be unique in workspace). description: Optional description for the pipeline.

Returns: Dictionary with status, pipeline_id, pipeline_name, workspace_name, and message.

Example: ```python # Create a blank pipeline result = create_blank_pipeline( workspace_name="Analytics Workspace", pipeline_name="My_Data_Integration_Pipeline", description="Pipeline for data integration workflows" )

# Later, add activities to it
add_copy_activity_to_pipeline(
    workspace_name="Analytics Workspace",
    pipeline_name="My_Data_Integration_Pipeline",
    source_type="AzurePostgreSqlSource",
    source_connection_id=conn_id,
    source_table_schema="public",
    source_table_name="customers",
    destination_lakehouse_id=lakehouse_id,
    destination_connection_id=lakehouse_conn_id,
    destination_table_name="customers"
)
```

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
workspace_nameYes
pipeline_nameYes
descriptionNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Tool Definition Quality

A4.4/5.0
Behavior3/5

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

With no annotations provided, the description carries full burden for behavioral disclosure. It clearly indicates this is a creation/mutation operation and mentions the pipeline starts with 'an empty activities array.' However, it doesn't disclose important behavioral aspects like required permissions, whether the operation is idempotent, error conditions, or rate limits that would be crucial for a creation tool.

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, parameters, returns, example) and front-loads the core functionality. The example is helpful but lengthy; some sentences could be more concise. Overall, most content earns its place by providing necessary guidance.

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 this is a creation tool with no annotations but with output schema (returns dictionary with specific fields), the description provides good context. It explains the purpose, parameters, return structure, and usage sequence. The main gap is lack of behavioral details like permissions or error handling that would be important for a creation operation.

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?

With 0% schema description coverage, the description fully compensates by providing detailed parameter documentation. It explains workspace_name as 'display name of the workspace,' pipeline_name as 'must be unique in workspace,' and description as 'optional description for the pipeline.' This adds substantial meaning beyond what the bare schema provides.

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 ('Create a blank Fabric pipeline with no activities') and resource ('Data Pipeline in the specified workspace'), distinguishing it from sibling tools like add_copy_activity_to_pipeline by emphasizing it creates an empty pipeline ready for later population. This provides explicit differentiation from related tools.

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

Usage Guidelines5/5

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

The description explicitly states when to use this tool ('ready to be populated with activities later using the add_copy_activity_to_pipeline tool') and provides a clear alternative workflow. The example further reinforces this guidance by showing the sequence of create_blank_pipeline followed by add_copy_activity_to_pipeline.

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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