Skip to main content
Glama
Sign In
Microsoft Fabric MCP Server

Microsoft Fabric MCP Server

by Jasuni69
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Remote

generate_fabric_code

Generate Fabric-specific PySpark code for lakehouse operations, including reading, writing, merging Delta tables, and monitoring performance. Use it to get ready-to-run code for Microsoft Fabric.

Instructions

Generate Fabric-specific PySpark code for lakehouse operations.

Args:
    operation: Type of operation ('read_lakehouse', 'write_lakehouse', 'merge_delta', 'performance_monitor')
    lakehouse_name: Name of the lakehouse
    table_name: Name of the source table
    target_table: Name of the target table (for write/merge operations)
    ctx: Context object containing client information
Returns:
    A string containing the generated Fabric-specific PySpark code.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "operation": "<operation>"
    },
    "name": "generate_fabric_code"
  }
}

Input Schema

NameRequiredDescriptionDefault
operationYes
table_nameNo
target_tableNo
lakehouse_nameNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A3.6/5.0
Behavior3/5

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

With no annotations provided, the description carries the full behavioral burden. It discloses that the tool returns a string and mentions a ctx object, but it does not state whether the generated code is executed or only returned, whether the tool has side effects, or whether a lakehouse/workspace context must be set beforehand.

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 docstring is compact, uses clear Args/Returns structure, and front-loads the purpose. The phantom ctx parameter is a minor structural blemish, but overall the description is appropriately sized and scannable.

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

Completeness3/5

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

The description covers the basics: what the tool does, its arguments, and return value. However, it does not explain how the parameters interact with the chosen operation, which parameters are needed for which operation type, or why ctx appears in the description but not in the schema. With no output schema, these gaps reduce completeness.

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

Parameters3/5

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. It adds real value for operation by listing allowed values and clarifies target_table's role in write/merge operations. However, it documents a ctx parameter that is absent from the input schema, and lakehouse_name and table_name are only minimally explained.

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 ('Generate') and resource ('Fabric-specific PySpark code for lakehouse operations'), making the tool's core purpose immediately clear. The enumerated operation types (read_lakehouse, write_lakehouse, merge_delta, performance_monitor) further differentiate it from sibling tools like generate_pyspark_code.

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 operation values imply the intended use cases, but there is no explicit guidance on when to choose this tool over alternatives such as generate_pyspark_code or validate_fabric_code. No exclusions, prerequisites, or 'use this instead when...' statements are provided.

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

Deploy Server

Other Tools