powerbi-desktop-mcp

classify_connections

Classifies every data source a Power BI model reads and flags gateway, local-path, credential, and refresh issues to surface dashboard risks.

Instructions

Every data source the model reads, classified (on-prem SQL Server, SharePoint site, personal OneDrive, web, local/UNC file, ...) with the problems that matter for SQL / SharePoint / OneDrive dashboards: gateway needed for service refresh, localhost or OneDrive-synced local paths (only work on this PC), http, personal-OneDrive dependence, dev/test names, the same target spelled several ways (separate credentials), and parameters resolved to their values.

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": "classify_connections"
  }
}

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

B3.3/5.0

Behavior4/5

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

With no annotations, the description carries the burden and does disclose meaningful behavior: it classifies source types and flags specific risks such as gateway requirements, local paths, HTTP, personal OneDrive dependence, duplicate targets, and resolved parameters. It does not state permissions or read-only behavior explicitly, but the classification purpose is clear and the output schema can carry return details.

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?

The description is front-loaded with the core purpose and then enumerates relevant problem categories efficiently. It is dense but appropriately sized for a diagnostic classifier, though the single long sentence is somewhat run-on.

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?

The output schema exists, so return values need not be explained, and the description sufficiently covers the kinds of problems detected. However, it does not explain the required instance input or provide explicit usage boundaries relative to the many sibling tools, leaving meaningful gaps for correct invocation.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters1/5

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 is never mentioned in the description. The description gives no meaning, format, or accepted values for instance, so it does not compensate for the schema gap.

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 clearly states the action and resource: classifying every data source the model reads. It also differentiates itself from simpler listing tools by emphasizing problem detection for SQL/SharePoint/OneDrive dashboards. It stops short of explicitly naming sibling alternatives such as list_data_sources or analyze_sharepoint_patterns.

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?

Usage is implied by the dashboard-audit and connection-problem framing, but there is no explicit guidance on when to use this versus list_data_sources or other diagnostics. The agent can infer that this is for connection-risk assessment, but alternatives and exclusions are not stated.

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