powerbi-desktop-mcp

analyze_relationships

Detect Power BI model relationship design issues by flagging many-to-many, bidirectional cross-filter, inactive relationships, and visible tables with no relationships.

Instructions

List relationships with modeling-analysis flags attached: which are many-to-many, which cross-filter bidirectionally, which are inactive, and which visible tables have no relationships at all (isolated from the rest of the model). Use this over list_relationships when the goal is spotting model design issues rather than just reading the raw list.

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": "analyze_relationships"
  }
}

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

A4.2/5.0

Behavior4/5

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

No annotations are provided, so the description carries the full burden: it discloses that this is a listing/analysis operation and spells out the four analytical dimensions returned, including the non-obvious isolated-tables detection. It does not state read-only nature outright (only implied by 'List') or note any cost/scope caveats, but the flag enumeration is substantive behavioral context.

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 action and the flag taxonomy, then immediately the differentiating routing sentence. Mild redundancy in the parenthetical 'isolated from the rest of the model', which restates 'have no relationships at all', but overall tight.

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?

An output schema exists, so return values need no explanation, and the design-issue framing fully covers when to reach for it. The only real gap is the undocumented 'instance' argument, which is the one thing an agent must supply.

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?

The single required parameter 'instance' has 0% schema description coverage, and the description says nothing about what an instance is, where it comes from, or what form it takes. With low coverage the description was expected to compensate and does not.

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 and resource ('List relationships with modeling-analysis flags attached') and enumerates exactly what the flags cover: many-to-many, bidirectional cross-filter, inactive, and isolated tables. It names the sibling it differs from (list_relationships) so an agent can route without opening either schema.

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

Usage Guidelines5/5

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

Explicitly says 'Use this over list_relationships when the goal is spotting model design issues rather than just reading the raw list' — a clear when-to-use plus a named alternative and the condition that selects it. Nothing left to inference.

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