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Datacube Sk

by io.github.pipeworx-io
OverviewAdmin

List Datasets

list_datasets
Read-onlyIdempotent

Browse / search the DATAcube collection of Slovak statistical cubes. Filter by case-insensitive substring of the English label. Returns each matching cube's code (use it as cube in the other tools), label, last update date, and its ordered list of dimension codes.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
langNoLabel language (default "en").
limitNoMax results to return (default 50, max 200).
queryNoCase-insensitive substring to match against dataset labels (e.g. "population", "unemployment"). Omit to list everything.

Schema Changelog

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

  1. Changed1 schema field changed
    • addedInput schema / examples
      Added value: +[
+  {
+    "query": "population"
+  },
+  {
+    "lang": "en",
+    "limit": 20,
+    "query": "unemployment"
+  }
+]
  2. First observed

TDQS

A4.3/5.0
Behavior4/5

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

Annotations declare readOnly, idempotent, openWorld, and non-destructive. The description adds behavioral details: case-insensitive substring matching, return fields including cube code usage in other tools. No contradictions.

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 concise sentences. Front-loaded with purpose, each part adds value. No wasted words.

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

Completeness5/5

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

No output schema, but description details all return fields (code, label, update, dimensions) and explains how to use cube code in other tools. Complete for a search 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 coverage is 100%, so baseline is 3. The description adds minimal extra meaning beyond schema for the query parameter (mentions English label) but doesn't elaborate on lang or limit.

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 clearly states the tool browses/searches a specific collection (DATAcube of Slovak statistical cubes) and details filtering and return fields. It distinguishes from siblings like dataset_dimensions.

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 explains filtering by case-insensitive substring and that omitting query lists all datasets. While it doesn't explicitly state when not to use, the context and sibling names imply its purpose.

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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