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Ilostat

by io.github.pipeworx-io
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Get Data

get_data
Read-onlyIdempotent

Pull observations from an ILOSTAT dataset. key is a dot-separated SDMX dimension filter, one position per dimension in the order given by dataflow_structure; leave a position empty to wildcard it. ILO keys typically start with REF_AREA then FREQ; e.g. get_data({ dataflow_id: "DF_SDG_0852_SEX_AGE_RT", key: "USA.A...", start_period: "2015", end_period: "2024" }) selects the USA, annual frequency, and wildcards the remaining dimensions. Omit key (or pass "") to fetch all series — caution, this can be large. Returns decoded series with their dimension labels and per-period values.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
keyNoDot-separated dimension filter (one position per dimension, empty = wildcard), e.g. "USA.A..." . Omit for all series.
last_nNoReturn only the last N observations per series.
end_periodNoEnd period, e.g. "2024".
max_seriesNoCap the number of decoded series returned (default 200).
dataflow_idYesILOSTAT dataflow id, e.g. "DF_SDG_0852_SEX_AGE_RT".
start_periodNoStart period, e.g. "2015", "2015-01", "2015-Q1".

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changed
    • addedInput schema / examples
      Added value: +[
+  {
+    "dataflow_id": "DF_SDG_0852_SEX_AGE_RT",
+    "end_period": "2024",
+    "key": "USA.A...",
+    "start_period": "2015"
+  },
+  {
+    "dataflow_id": "DF_SDG_0852_SEX_AGE_RT",
+    "end_period": "2023",
+    "key": "FRA.A.M.15-24.",
+    "last_n": 5,
+    "start_period": "2020"
+  }
+]
  2. First observed

TDQS

A4.7/5.0
Behavior5/5

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

Annotations already mark readOnly, idempotent, openWorld, non-destructive. Description adds key behavioral traits: syntax for key, wildcard mechanism, large data caution, and return format (decoded series with labels and per-period values). No contradiction.

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

Conciseness4/5

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

Slightly long but well-structured with front-loaded purpose. Each sentence adds value, including example and caution. No redundancy.

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?

Given 6 parameters, 100% schema coverage, and no output schema, the description is complete: explains key format, provides examples, notes default max_series (200), and describes return format. No gaps.

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 description adds critical meaning: explains dot-separated SDMX dimension filter order, gives explicit examples, and clarifies that omitting key fetches all. This goes beyond the 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 specifies a clear action (pull observations) and resource (ILOSTAT dataset). It distinguishes from sibling tools like dataflow_structure and list_dataflows by focusing on data retrieval with a specific key syntax.

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?

Provides clear context on when to use (after obtaining dataflow_id) and when to omit key (all series, with caution). Lacks explicit mention of alternatives but the sibling list shows no direct duplicate, so the guidance is adequate.

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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