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PowerBI MCP Server

by gurvinder-dhillon
OverviewSchemaRelated ServersScoreDiscussions
Python
Remote

get_refresh_history

Retrieve recent refresh history for a PowerBI dataset to monitor status, timestamps, and error details. Use to track data freshness and troubleshoot refresh failures.

Instructions

Get refresh history for a PowerBI dataset.

Shows recent refresh operations including:

  • Status (Completed, Failed, Unknown)

  • Start and end times

  • Request ID and refresh type

  • Error messages (for failed refreshes)

Useful for monitoring data freshness and troubleshooting refresh failures.

Parameters:

  • dataset_id (required): The dataset ID

  • workspace_id (optional): Workspace ID. Omit for datasets in "My workspace"

  • top: Number of refresh records to return (default: 5, max: 60)

  • format: Response format - "markdown" or "json" (default: "markdown")

Returns: Formatted refresh history with status, timestamps, and error details for failed refreshes.

Example usage:

  • Get last 5 refreshes: dataset_id="abc123"

  • Get last 10 refreshes: dataset_id="abc123", top=10

  • Check specific workspace: dataset_id="abc123", workspace_id="workspace456"

Error handling:

  • If dataset_id not found, verify the ID using get_datasets

  • For permission errors, ensure service principal has read access

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
dataset_idYes
workspace_idNo
topNo
formatNomarkdown

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Tool Definition Quality

A4.6/5.0
Behavior5/5

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

No annotations provided, so description fully covers behavior: lists returned fields, includes default values/max for top, mentions error cases, and implies read-only nature.

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?

Well-structured with sections, but slightly verbose; front-loaded with purpose, though some redundancy exists (e.g., repeating 'Get refresh history').

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?

Output schema exists, so return field details aren't needed; description gives sufficient overview of returns and covers usage comprehensively. Minor gap: no mention of pagination or rate limits.

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

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

With 0% schema description coverage, the description explains all 4 parameters in detail (required/optional, defaults, format options) and adds usage context beyond schema.

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?

Clear verb+resource ('Get refresh history for a PowerBI dataset') and distinguishes from siblings like get_dataset or query_dataset by focusing on refresh operations.

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?

States use cases (monitoring data freshness, troubleshooting failures) and provides examples and error handling, but does not explicitly contrast with alternatives.

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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