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ILO Labour Statistics (ILOSTAT)

by io.github.SidneyBissoli

Get ILOSTAT data

ilo_get_data
Read-onlyIdempotent

Statistical observations from one ILOSTAT dataflow, filtered by dimension codes (filters, e.g. {"REF_AREA": ["BRA","ARG"], "SEX": "SEX_T"}) and period (start_period/end_period, e.g. "2015"/"2024"). REF_AREA is required, maximum 30 areas per call — for broad panels, split areas into batches and/or paginate by period. Unfiltered dimensions return all their categories. Does not aggregate, convert or otherwise transform values (raw ILOSTAT data only), and does not search indicators (use ilo_search_indicators).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
filtersNoDimension id → code or list of codes (from ilo_list_dimension_values). REF_AREA is required (up to 30 area codes).
dataflowYesDataflow id from ilo_search_indicators (e.g. "DF_UNE_DEAP_SEX_AGE_RT")
end_periodNoLast period, e.g. "2024"
start_periodNoFirst period, e.g. "2015"
provenance_modeNoProvenance verbosity: 'concise' (default — source, url, vintage, retrieval date, citation, license) or 'detailed' (full canonical block with dataset, dimension key and notices)
last_n_observationsNoAlternative to periods: only the latest N observations per series

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
rowsYes
columnsYes
dataflowYes
provenanceYes
rows_countYes
attributionYes

Schema Changelog

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

  1. First observed

TDQS

A4.7/5.0
Behavior5/5

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

Annotations already declare readOnly, idempotent, and non-destructive behavior, so the description only needs to add context beyond that. It adds valuable behavioral detail: no aggregation, no transformation, raw values only, unfiltered dimensions return all categories, and a 30-area cap with batching/pagination guidance. No contradictions with 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?

Four sentences deliver purpose, filter examples, constraints, batching strategy, raw-data semantics, and sibling routing with no filler. The core scope is front-loaded, and every sentence earns its place.

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?

Combined with a fully documented input schema and an output schema, the description covers intended use, required parameters, operational limits, batching strategy, raw-data behavior, and sibling differentiation. There is no critical missing information for an agent to select and call this tool correctly.

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

Parameters4/5

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

Schema coverage is 100%, so the baseline is 3. The description adds meaning by giving concrete filter/period examples, enforcing REF_AREA as required, explaining the 30-area limit, and clarifying what happens when dimensions are unfiltered. It leaves last_n_observations and provenance_mode to the schema, but those are already well documented there.

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 opens with a specific action and object: retrieving statistical observations from one ILOSTAT dataflow, filtered by dimension codes and period. It explicitly scopes behavior as raw ILOSTAT data and distinguishes itself from ilo_search_indicators, making its purpose unmistakable.

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 gives clear operational guidance: REF_AREA is required, max 30 areas per call, batch for broad panels, paginate by period, and unfiltered dimensions return all categories. It names ilo_search_indicators as the alternative for indicator search, though it does not explicitly route to ilo_get_indicator_metadata or ilo_list_dimension_values.

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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TDQS

A4.6/5.0
Disambiguation4/5

The ilo_* tools are clearly role-separated: get data, inspect metadata, list dimension codes, and search indicators. The only real ambiguity is between `search` and `ilo_search_indicators`, which both query the same catalogue, though the descriptions clarify that one returns documents for `fetch` while the other returns dataflow ids for data queries.

Naming Consistency4/5

Most tools follow a predictable `ilo_<verb>_<object>` pattern: get_data, get_indicator_metadata, list_dimension_values, search_indicators. `fetch` and `search` break that pattern, but this is an explicitly forced adapter to the OpenAI Deep Research contract rather than random inconsistency.

Tool Count5/5

Six tools are well-scoped for the stated domain: the search/fetch pair handles document retrieval, and the four `ilo_*` tools cover indicator discovery, metadata inspection, dimension-value lookup, and raw statistical data extraction. No tool feels redundant or unnecessary.

Completeness4/5

The set covers the full workflow from finding an indicator dataflow, inspecting its structure, listing valid codelist values, and retrieving raw data. A minor gap is that `ilo_list_dimension_values` suggests using `search` to resolve a name like 'Brazil' to a code, but `search` actually returns catalogue documents rather than codelist codes, so name-to-code resolution lacks a direct tool.

Resources