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Czso Cz

by io.github.pipeworx-io
OverviewAdmin

Get Data

get_data
Read-onlyIdempotent

Fetch the actual observations/values for a ČSÚ dataset as JSON-stat 2.0 (dimensions in id/dimension, cell counts in size, numbers in value). Verified live. NOTE: returns the complete dataset as a full cross-product, which is often large (hundreds of thousands of cells, 1MB+) — call data_summary first to check pocetUdaju. Version (verze) is auto-resolved from the catalog if omitted.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
kodYesDataset id, e.g. "OBY05B03". Get it from list_datasets.
langNoAccept-Language for labels, e.g. "en" or "cs" (default). Most category labels are Czech-only regardless.
verzeNoDataset version. Optional — defaults to the latest version from the catalog.

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: +[
+  {
+    "kod": "OBY05B03"
+  },
+  {
+    "kod": "OBY05B03",
+    "lang": "en",
+    "verze": "1.0"
+  }
+]
  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, openWorldHint, idempotentHint true. The description adds that the result is a full cross-product that can be large (hundreds of thousands of cells, 1MB+) and that version is auto-resolved, providing beyond-annotation context.

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: first states purpose and format, second adds usage guidance and size warning. No redundant words, front-loaded with key information.

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?

Describes return format (JSON-stat 2.0 with dimensions, cell counts, numbers), warns about large size, and suggests a precursor tool. Despite no output schema, this is thorough for a complex data-fetching 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%. The description explains `kod` as dataset ID from `list_datasets`, notes `lang` labels are mostly Czech-only, and states `verze` auto-resolves. This adds insight beyond schema descriptions.

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 fetches observations/values for a ČSÚ dataset in JSON-stat 2.0 format, with specific fields like `id`/`dimension`, `size`, and `value`. This distinguishes it from siblings like `list_datasets` and `data_summary`.

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?

It advises calling `data_summary` first to check `pocetUdaju` and notes auto-resolution of version. While it doesn't explicitly state when not to use, the guidance is clear and useful.

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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