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PowerBI Analyst MCP

PowerBI Analyst MCP

by mbrummerstedt
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

get_dataset_info

Retrieve detailed metadata and refresh history for Power BI datasets to monitor data freshness and understand dataset structure.

Instructions

Return detailed metadata for a single Power BI dataset.

Includes name, owner, refresh schedule, storage mode, web URL, and more. Also returns the last 5 refresh history entries so you can see data freshness.

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": {
      "workspace_id": "<workspace_id>",
      "dataset_id": "<dataset_id>"
    },
    "name": "get_dataset_info"
  }
}

Input Schema

NameRequiredDescriptionDefault
workspace_idYes
dataset_idYes

Output Schema

NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.2

TDQS

A3.8/5.0
Behavior4/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It successfully documents return content specifics (name, owner, refresh schedule, storage mode) and crucially discloses the 'last 5 refresh history entries' limit, which explains data freshness capabilities. It lacks mention of auth requirements or rate limits, but covers the primary behavioral contract.

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?

Three sentences with zero waste: first states purpose, second details metadata fields, third specifies refresh history behavior. Information is front-loaded and every sentence earns its place.

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?

Given the output schema exists, the description appropriately summarizes return values without redundancy. However, for a tool with required parameters and 0% schema coverage, the complete omission of workspace_id and dataset_id explanations leaves a significant documentation gap that the description should have addressed.

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% (titles only). The description mentions 'single Power BI dataset' which conceptually maps to dataset_id, but completely omits workspace_id despite it being a required parameter. With zero schema coverage, the description fails to compensate by explaining either parameter's purpose or format.

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?

The description clearly states the tool 'Return[s] detailed metadata for a single Power BI dataset' — specific verb (return), specific resource (metadata), and scope (single dataset). It effectively distinguishes from sibling 'list_datasets' by emphasizing 'single' versus list operations.

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?

While the description implies this tool is for detailed retrieval versus the sibling 'list_datasets', it provides no explicit guidance on when to use this over alternatives, prerequisites (e.g., obtaining IDs from list_workspaces/list_datasets first), or when-not-to-use scenarios.

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

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