powerbi-desktop-mcp

list_all_dax_expressions

Retrieves every DAX expression in a Power BI model—measures, calculated columns, and calculated tables—so you can audit or review all formulas in one place.

Instructions

Get every piece of DAX in the model in one place: measures, calculated columns, and calculated tables, each with its full expression. Useful for an overall DAX audit/review rather than pulling measures and columns separately.

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_all_dax_expressions"
  }
}

Input Schema

Name	Required	Description	Default
`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

A3.7/5.0

Behavior3/5

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

No annotations are provided, so the description carries the full disclosure burden. 'Get' and 'list' strongly imply a read-only enumeration and the description makes clear the output covers all three DAX object types, but it says nothing about permission requirements, cost/size of the response on large models, or whether the scan is expensive. Adequate for a simple read tool, but not rich.

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?

Front-loaded with the core scope ('every piece of DAX in the model') followed by the enumeration and then the usage cue. Three tight sentences with minimal filler, though 'in one place' is slightly redundant with the list itself.

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 return-value explanation is not needed, and the description covers what is included in the result set. However, it omits the only parameter ('instance') entirely and gives no signal about result volume on a large model, leaving a gap for an agent deciding how to call it.

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%: the single required parameter 'instance' has no description in the schema, and the tool description never mentions it at all. With one undocumented parameter, the description fails to compensate for the coverage gap, leaving the agent to guess what 'instance' refers to.

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?

States a specific verb ('Get every piece of DAX') and enumerates the exact resources returned: measures, calculated columns, and calculated tables with full expressions. It also distinguishes itself from siblings by framing the result as a consolidated view rather than 'pulling measures and columns separately', which differentiates it from list_measures and list_columns.

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

Usage Guidelines4/5

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

Gives a clear use case: 'overall DAX audit/review rather than pulling measures and columns separately', which effectively routes the agent away from the narrower sibling tools. It lacks explicit exclusions (e.g., when to prefer run_dax_query or list_measures), so it stops just short of a full when/when-not treatment.

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