powerbi-desktop-mcp

list_columns

Retrieve columns across a Power BI model or from a specified table, including data type, hidden status, and calculated-column DAX expressions for model inspection.

Instructions

List columns across the model, or for one table if table is given. Includes data type, hidden flag, and calculated-column DAX expression where applicable.

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": {
      "instance": "<instance>"
    },
    "name": "list_columns"
  }
}

Input Schema

Name	Required	Description	Default
`table`	No
`instance`	Yes

Output Schema

Name	Required	Description	Default
No arguments

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.0Oct 23, 2026

TDQS

A3.5/5.0

Behavior3/5

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

With no annotations, the description carries the full burden; it implies read-only via "List" and discloses the shape of what comes back (data type, hidden flag, DAX expression for calculated columns). It says nothing about permissions, whether hidden columns are included by default, or model/instance scoping — gaps relevant for a tool with zero annotation coverage.

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?

Two short sentences, front-loaded with the scope decision and followed by the returned fields. Nothing is wasted and no sentence restates the name.

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?

An output schema exists, so explaining return values is not required, and the description does just enough on scope. However, for a two-parameter tool whose schema documents neither parameter, leaving the required `instance` argument entirely undefined is a meaningful omission.

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 explains `table` well as an optional narrowing filter, but `instance` — a required parameter — is never clarified anywhere, leaving half the parameter surface undefined.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description gives a specific verb and resource ("List columns") plus its scope (whole model or one table), so an agent can immediately tell it apart from list_tables or list_measures by resource. It stops short of naming any sibling or stating an explicit contrast, so it lands just below the top band.

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 two operating modes are stated clearly: omit `table` for the whole model, supply it for a single table. Beyond that there is no guidance on when to prefer this tool over list_all_dax_expressions, find_unused_fields, or profile_table, and no exclusions or prerequisites are given.

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