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mob999

@mob999/cube_mcp

by mob999

execute_query

Run multi-dimensional analytical queries on discovered entities to compute measures, filter by dimensions, sort results, and apply date ranges for deeper data insights.

Instructions

Execute an Analytical Query. Perform multi-dimensional analysis on one of the entities discovered via 'discover_entities'.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoMax rows to return (default None).
orderNoOptional multi-column sort rules applied in order.
totalNoOptional Cube total flag.
offsetNoOptional row offset.
filtersNoOptional filters to apply to the query.
measuresNoMeasures to calculate (e.g., ['Components.area']). MUST use 'Entity.Measure' format.
rowLimitNoOptional Cube rowLimit.
segmentsNoOptional Cube segments.
timezoneNoOptional query timezone, for example 'UTC' or 'Asia/Shanghai'.
ungroupedNoOptional Cube ungrouped flag.
dimensionsNoDimensions to group/segment by (e.g., ['Components.id']). MUST use 'Entity.Dimension' format.
renewQueryNoOptional Cube renewQuery flag.
entity_nameYesThe name of the Entity to query (e.g., 'Components').
responseFormatNoOptional Cube response format.
timeDimensionsNoOptional Cube time dimensions.

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observedv1.0.7

TDQS

A3.6/5.0
Behavior2/5

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

No annotations are provided, so the description carries the full burden of disclosing behavioral traits. 'Execute an Analytical Query' and 'multi-dimensional analysis' imply a read-oriented computation, but the description does not mention whether any state changes occur, how results are returned, pagination behavior, or potential performance/rate-limit concerns. For a query tool this is a notable gap.

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?

Two sentences with no filler. The core purpose is front-loaded, and the discover_entities dependency is stated in the second sentence. Every word earns its place.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

This is a complex tool with 15 parameters, no output schema, and no annotations. The description covers the prerequisite workflow (discover_entities) but omits any guidance on the return format, required parameter combinations, or the analytical intent behind the many Cube flags. An agent would need to rely heavily on the schema alone, which is not enough for such a configurable tool.

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

Parameters3/5

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

Schema description coverage is 100%, so the schema already documents all 15 parameters, establishing a baseline of 3. The description adds a meaningful connection between entity_name and the discover_entities workflow, but it does not meaningfully clarify the many Cube-specific options (filters, timeDimensions, renewQuery, etc.) beyond what the schema already provides.

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 states a specific verb ('Execute') and resource ('Analytical Query'), and further clarifies with 'Perform multi-dimensional analysis on one of the entities discovered via discover_entities.' This clearly distinguishes it from the only sibling, discover_entities, which focuses on discovery rather than querying.

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?

The description explicitly frames the tool as operating on entities discovered via 'discover_entities', establishing a clear prerequisite and workflow. It tells the agent when this tool is appropriate, though it does not enumerate explicit alternatives or negative cases. Since the sibling set is minimal, this is sufficient guidance.

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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