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Datacube Sk

by io.github.pipeworx-io
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Get Data

get_data
Read-onlyIdempotent

Pull data for a cube as JSON-stat. Provide one selection per dimension, in the dimension order returned by dataset_dimensions. Each selection is either a single value code, a comma-separated list of codes (e.g. "SEX_M,SEX_F"), or the literal "all" for every value of that dimension. The number of selections must equal the cube's dimension count. Output is JSON-stat: value is the flat data array, dimension/size/id describe its shape.

Example tool call

This call uses example arguments supplied in the tool’s input schema.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "cube": "as1001rs",
      "selections": [
        "2024",
        "UKAZ01",
        "all"
      ]
    },
    "name": "get_data"
  }
}

Input Schema

NameRequiredDescriptionDefault
cubeYesCube code, e.g. "as1001rs" (from list_datasets).
langNoLabel language (default "en").
selectionsYesOne entry per dimension, in dataset_dimensions order. Each is a value code, a comma-separated list of codes, or "all". Example for as1001rs: ["2024", "UKAZ01", "all"].

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changed
    • addedInput schema / examples
      Added value: +[
+  {
+    "cube": "as1001rs",
+    "selections": [
+      "2024",
+      "UKAZ01",
+      "all"
+    ]
+  },
+  {
+    "cube": "as1001rs",
+    "selections": [
+      "2023,2024",
+      "UKAZ01,UKAZ02",
+      "SEX_M"
+    ]
+  }
+]
  2. First observed

TDQS

A4.6/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=true and destructiveHint=false. The description adds behavioral detail by explaining the output format (JSON-stat) and constraints on selections, adding value beyond annotations.

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 concise, front-loaded with the main purpose, then details. Every sentence is informative and well-structured.

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?

Despite no output schema, the description fully explains the output structure (JSON-stat, value array, dimension/size/id) and covers input constraints. It is complete for a data retrieval tool.

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

Parameters5/5

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

Schema coverage is 100%, but the description adds significant meaning: the format of selections (codes, comma-separated, 'all'), the requirement that number of selections equals dimension count, and the reference to dataset_dimensions for order.

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 pulls data for a cube as JSON-stat, specifying the input format and output structure. It distinguishes from siblings by referencing dataset_dimensions for dimension order.

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 provides explicit usage details: provide selections per dimension, in order from dataset_dimensions, with examples. It lacks explicit when-not or alternatives but is contextually clear.

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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