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PowerBI Analyst MCP

PowerBI Analyst MCP

by mbrummerstedt
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

list_columns

Retrieve column details from Power BI datasets, including names, tables, data types, and key column status, with optional table filtering.

Instructions

List columns (dimensions) in a Power BI dataset.

Returns each column's name, parent table, description, data type, and whether it is a key column. 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_columns"
  }
}

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

A4/5.0
Behavior4/5

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

With no annotations provided, the description carries full burden and successfully discloses return structure (5 specific fields returned) and filtering behavior. It appropriately describes the output content since structured output schema exists. Minor gap: no mention of error behavior (e.g., empty dataset) or permission requirements, but core behavior is transparent.

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?

Three sentences with zero waste: sentence 1 states purpose, sentence 2 documents return values, sentence 3 covers optional filtering. Front-loaded with the primary action, no redundant or marketing language. Efficient information density.

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 the simple 3-parameter schema (2 required, 1 optional, no nesting) and existence of output schema, the description provides appropriate coverage. It explains the tool's scope, return summary, and key optional functionality. Could improve by explicitly noting required parameters given 0% schema coverage, but sufficient for agent selection.

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%, requiring the description to compensate. It successfully explains the optional table_name parameter ('Optionally filter by table name'), but does not explicitly document workspace_id or dataset_id. However, these are somewhat implied by 'Power BI dataset' context and standard hierarchical API patterns, making this minimally adequate rather than deficient.

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?

Description clearly states 'List columns (dimensions) in a Power BI dataset' - specific verb, resource, and domain. The parenthetical '(dimensions)' effectively distinguishes this from sibling tool list_measures (which handles calculated measures) and list_tables (containers), clarifying this returns column-level metadata.

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?

Description implies usage through return value enumeration (name, data type, key status) and mentions 'Optionally filter by table name,' suggesting when to use the table_name parameter. However, it lacks explicit guidance on when to choose this over list_tables (schema exploration flow) or prerequisites like needing workspace_id/dataset_id from prior calls.

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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