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PowerBI Analyst MCP

by mbrummerstedt
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TDQS

A4.2/5.0

Scored across 13 tools

Disambiguation5/5

Every tool has a distinct, well-defined purpose with no overlap. For example, list_datasets enumerates datasets, get_dataset_info provides metadata for a specific dataset, execute_dax runs queries, and read_query_result handles pagination of saved results. The tools are clearly differentiated by their specific functions within the Power BI domain.

Naming Consistency5/5

All tool names follow a consistent verb_noun pattern using snake_case. Examples include authenticate, delete_query_log_entry, execute_dax, get_dataset_info, list_apps, list_columns, list_datasets, list_measures, list_tables, list_workspaces, logout, read_query_result, and search_query_history. This uniformity makes the tool set predictable and easy to navigate.

Tool Count5/5

With 13 tools, the count is well-scoped for a Power BI analytics server. It covers authentication, dataset exploration, query execution, result handling, and history management without being overwhelming. Each tool serves a clear purpose, such as listing resources, executing DAX, or managing logs, making the set comprehensive yet manageable.

Completeness4/5

The tool set provides strong coverage for core Power BI workflows, including authentication, dataset listing and inspection, DAX query execution, and result pagination. Minor gaps exist, such as the lack of tools for creating or modifying datasets, reports, or dashboards, but these are not essential for the stated analyst focus. The tools support a complete query and exploration lifecycle without dead ends.

Maintenance

ActivityInactive
ResponsivenessUnresponsive

Resources