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KuudoAI

Amazon Selling Central MCP

by KuudoAI

DataKiosk_getQueries

Retrieve details for Data Kiosk queries by applying filters. Use it to audit, monitor, or review query activity in your Amazon Selling Central deployment.

Instructions

Catalog entry. Runs in your Kuudo deployment, not here. Returns details for the Data Kiosk queries that match the specified filters.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

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

  1. First observedv1.0.0

TDQS

C2.9/5.0
Behavior3/5

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

No annotations are present, so the description carries the behavioral disclosure burden. It does state that the tool 'Runs in your Kuudo deployment, not here' and that it 'Returns details,' which implies a read-only listing operation. However, it does not disclose pagination behavior, output shape, authorization needs, or explicitly confirm the absence of side effects.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness3/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is short, but it is structurally awkward: the vague 'Catalog entry' fragment comes first, followed by a runtime caveat, and only then the actual purpose. The core function is not front-loaded, and 'Catalog entry' adds little actionable value.

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?

For a listing operation with no output schema and no annotations, the description is incomplete. It does not explain available filters, pagination, defaults, or what 'details' means. The runtime note is useful context, but an agent still lacks enough information to invoke the tool correctly with filters.

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?

The input schema declares zero named parameters and allows additional properties, so the only parameter-related meaning comes from 'specified filters.' This tells an agent that filters are supported but not which filter names, types, or formats are valid. The description adds conceptual value beyond the empty schema but leaves invocation details guesswork.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly identifies the operation: returning details for Data Kiosk queries that match filters. The plural 'queries' and the resource name help distinguish it from DataKiosk_getQuery. However, it doesn't explicitly contrast with sibling tools, and the 'Catalog entry' preamble adds confusion.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description gives no guidance about when to use this tool versus DataKiosk_getQuery, DataKiosk_createQuery, or DataKiosk_cancelQuery. There are no prerequisites, exclusions, or alternative routing hints. Usage context is only implied by the name and the generic filter mention.

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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