powerbi-desktop-mcp

list_tables

Lists all tables in a Power BI data model, including visibility and description metadata where available, so you can inspect model structure before analysis or edits.

Instructions

List every table in the data model, with visibility/description metadata where available.

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_tables"
  }
}

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

C2.7/5.0

Behavior2/5

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

No annotations exist, so the description carries the full burden. It implies a read-only listing and hints that some tables may lack visibility/description metadata ('where available'), but says nothing about whether hidden tables are included, prerequisites on the instance, or permissions.

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?

A single front-loaded sentence with no filler; the 'where available' hedge is meaningful, not padding. Slightly more could be said without waste.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The presence of an output schema relieves the description of explaining return values, but with no annotations and an entirely undocumented required parameter, the definition leaves key invocation details unresolved.

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?

One required parameter ('instance') has 0% schema description coverage and is never mentioned in the description. The agent cannot tell from either field whether instance means a PBIX file, a connection, or a model ID.

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?

States a specific verb and resource ('List every table in the data model') and adds the scope of returned metadata. It is clearly distinct from siblings like list_columns and list_measures by resource, though it never names an alternative explicitly.

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?

There is no guidance on when to call this versus alternatives such as list_columns, get_model_summary, or analyze_model_size. The agent must infer usage purely from the resource name.

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