List the six SPARKS dimensions
list_sparks_dimensionsSPARKS resolves six dimensions — SKU, Pack, Amount, Region, Kernel, and Standard — a CPG industry first.
Input Schema
TableJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
MCP CPG Agent Protocols
TDQS
A4.2/5.0
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must convey behavior. It indicates a read-only query returning a predefined set of six dimensions, which is sufficient for a simple list tool. No side effects are implied, but the use of 'resolves' is slightly ambiguous.
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?
The description is a single clear sentence, front-loaded with the core purpose. Every word adds value; no wasted text.
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?
For a tool with zero parameters, no output schema, and low complexity, the description covers the essential: it lists the dimensions. It could mention the return format or data type, but it is sufficiently complete given the tool's simplicity.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has zero parameters, so schema coverage is 100%. The description adds no parameter info, but none is needed. Baseline 4 is appropriate since parameters are absent.
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 explicitly states the tool lists the six SPARKS dimensions, using a specific verb (list) and resource (dimensions), and names each dimension. This clearly distinguishes it from sibling tools like resolve_sparks_dimension.
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?
The description implies the tool is used to retrieve the list of dimensions, but does not explicitly state when to use it vs. resolve_sparks_dimension or other siblings. No exclusions or prerequisites are mentioned.
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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TDQS
A3.5/5.0
Disambiguation5/5
Each tool targets a distinct resource (headers, protocols, members, signals, dimensions) with clear actions (get, list, resolve). There is no overlap in functionality; even the two protocol tools differ in granularity (list vs. get).
Naming Consistency4/5
Naming follows a consistent verb_noun pattern using underscores and lowercase. Minor inconsistency: 'list_sparks_dimensions' uses plural while 'resolve_sparks_dimension' uses singular, but overall the pattern is clear.
Tool Count5/5
With 9 tools covering headers, protocols, members, signals, and dimensions, the count is well-proportioned for the domain. Each tool addresses a specific query type without unnecessary bloat or gaps.
Completeness5/5
The tool surface provides complete read access to all canonical entities: headers, protocols, members, signals, and dimensions. Listing and resolving operations are covered for each entity, matching the server's stated purpose of querying agent protocol data.