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Microsoft Fabric MCP Server

Microsoft Fabric MCP Server

by Jasuni69
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Remote

list_semantic_models

List all semantic models within a specified Microsoft Fabric workspace to quickly review available data models for analysis and reporting.

Instructions

List all semantic models in a Fabric workspace.

Args:
    workspace: Name or ID of the workspace (optional)
    ctx: Context object containing client information

Returns:
    A string containing the list of semantic models or an error message.

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "list_semantic_models"
  }
}

Input Schema

NameRequiredDescriptionDefault
workspaceNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A4/5.0
Behavior3/5

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

There are no annotations, so the description carries the behavioral burden. It discloses that the return value is a string and that errors are returned as messages, and 'List' implies a read-only operation. However, it does not mention pagination, limits, side effects, or how the optional workspace is resolved.

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

Conciseness5/5

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

The main purpose is front-loaded in a single clear sentence, followed by a compact Args/Returns block. Every line earns its place and there is no filler or redundancy.

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?

For a simple one-parameter listing tool, the description provides adequate purpose, arguments, and return behavior. However, it does not explain what happens when workspace is omitted, especially given sibling tools like set_workspace and clear_context that imply a current-workspace concept. This ambiguity is a clear gap.

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 compensate. It adds meaningful semantics by stating that workspace accepts a name or ID and is optional, and it documents ctx as a context object. This goes well beyond the schema's bare nullable string property.

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 states a specific verb ('List'), a specific resource ('all semantic models'), and a scope ('in a Fabric workspace'). The 'all' qualifier also differentiates it from singular retrieval siblings like get_semantic_model.

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 usage is implied by the imperative 'List all semantic models' — use it when you need an inventory of semantic models. However, it does not explicitly say when to prefer this over alternatives like get_semantic_model or get_model_schema, nor does it clarify behavior when workspace is omitted.

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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