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Jasuni69

Microsoft Fabric MCP Server

by Jasuni69

create_data_pipeline

Create a Data Pipeline in a Microsoft Fabric workspace with custom activities and dependencies. Orchestrate multi-stage workflows like Bronze → Silver → Gold for reliable data transformation.

Instructions

Create a Data Pipeline in a Fabric workspace with custom activities and dependencies.

Perfect for orchestrating medallion architecture workflows (Bronze → Silver → Gold).

Args:
    pipeline_name: Name for the pipeline
    pipeline_definition: Pipeline JSON definition with activities and dependencies
    workspace: Workspace name or ID (optional, uses context if not provided)
    description: Optional description for the pipeline
    ctx: Context object

Returns:
    Dictionary with pipeline details including ID and status

Example - Bronze to Silver to Gold cascade:
    pipeline_definition = {
        "properties": {
            "activities": [
                {
                    "name": "Bronze_Ingestion",
                    "type": "Notebook",
                    "typeProperties": {
                        "notebook": {"name": "bronze_ingest_notebook"}
                    },
                    "dependsOn": []
                },
                {
                    "name": "Silver_Transform",
                    "type": "Notebook",
                    "typeProperties": {
                        "notebook": {"name": "silver_transform_notebook"}
                    },
                    "dependsOn": [
                        {
                            "activity": "Bronze_Ingestion",
                            "dependencyConditions": ["Succeeded"]
                        }
                    ]
                },
                {
                    "name": "Gold_Transform",
                    "type": "Notebook",
                    "typeProperties": {
                        "notebook": {"name": "gold_transform_notebook"}
                    },
                    "dependsOn": [
                        {
                            "activity": "Silver_Transform",
                            "dependencyConditions": ["Succeeded"]
                        }
                    ]
                }
            ]
        }
    }

    create_data_pipeline(
        pipeline_name="Medallion_ETL_Pipeline",
        pipeline_definition=pipeline_definition,
        workspace="PROD-Analytics",
        description="Orchestrates Bronze → Silver → Gold transformations"
    )

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
workspaceNo
descriptionNo
pipeline_nameYes
pipeline_definitionYes

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observedv0.1.0

TDQS

A4.2/5.0
Behavior3/5

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

With no annotations, the description carries the behavioral burden. It discloses that the tool returns a dictionary with ID and status, and that workspace falls back to context, but it does not discuss idempotency, overwrite behavior, permission requirements, or failure modes for a mutation 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 purpose, args, returns, and an example. The example is long but earns its place by clarifying the nested pipeline_definition format. No section feels redundant, though the 'Perfect for...' sentence is slightly promotional.

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 four-parameter tool with a complex nested object and no output schema, the description is largely complete: it explains all parameters, illustrates the nested structure, and states the return value. It could be improved by noting potential validation failures or how an existing pipeline name is handled.

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%, and the description fully compensates. Every parameter is explained, and the detailed pipeline_definition example with activities and dependsOn gives an agent a concrete template for constructing valid input.

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: 'Create a Data Pipeline in a Fabric workspace with custom activities and dependencies.' This clearly differentiates it from sibling tools like pipeline_run or get_pipeline_definition, and the medallion architecture mention adds a concrete use case.

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?

The description gives a clear context for use: 'Perfect for orchestrating medallion architecture workflows (Bronze → Silver → Gold).' It does not explicitly mention alternatives or exclusions, but the creation-focused wording and example make the intended usage unambiguous.

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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