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PowerBI Analyst MCP

PowerBI Analyst MCP

by mbrummerstedt
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

list_measures

Retrieve Power BI dataset measures with names, tables, descriptions, and formats. Filter by table to analyze data models and understand calculations.

Instructions

List measures defined in a Power BI dataset.

Returns each measure's name, parent table, description, and format string. Optionally filter by table name.

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": {
      "workspace_id": "<workspace_id>",
      "dataset_id": "<dataset_id>"
    },
    "name": "list_measures"
  }
}

Input Schema

NameRequiredDescriptionDefault
workspace_idYes
dataset_idYes
table_nameNo

Output Schema

NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.2

TDQS

B3.4/5.0
Behavior3/5

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

With no annotations provided, the description carries the full burden. It successfully discloses return values (name, parent table, description, format string) and filtering behavior, but fails to state whether the operation is read-only/safe or describe pagination/error behaviors.

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 description is optimally structured with three efficient sentences: purpose first, return values second, parameter behavior third. Every sentence earns its place with zero redundancy or waste.

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?

Given the presence of an output schema, the description appropriately focuses on purpose and high-level behavior rather than return structure details. However, with zero schema descriptions and no annotations, the omission of required parameter semantics (workspace_id, dataset_id) leaves notable gaps for a three-parameter tool.

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

Parameters2/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, requiring the description to compensate. It explains the optional table_name parameter ('Optionally filter by table name'), but provides no semantic context for the two required parameters (workspace_id, dataset_id), leaving critical identifiers undocumented.

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 provides a specific verb (List) and resource (measures) with clear scope (Power BI dataset). It effectively distinguishes from siblings like list_columns or list_tables by specifying the exact metadata resource being retrieved.

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

Usage Guidelines2/5

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

The description lacks explicit guidance on when to select this tool versus alternatives like list_columns or get_dataset_info. While it mentions optional filtering by table_name, this describes parameter behavior rather than tool selection criteria or prerequisites.

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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