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ILO Labour Statistics (ILOSTAT)

by io.github.SidneyBissoli

Search ILOSTAT indicators

ilo_search_indicators
Read-onlyIdempotent

Search the ILOSTAT catalogue of ~1,200 indicator dataflows by keywords in the name or id (e.g. "unemployment rate sex age"). All terms must match (AND, case-insensitive), so start with 2–3 English words and drop terms if you get 0 results; results are ranked by ILO relevance weight, not by match count. Reading the id tells you the shape: suffix _RT = rate/ratio, NB = number (usually thousands); dataflows whose second token starts with 2 (e.g. DF_UNE_2EAP…) are ILO modelled estimates with full country/year coverage, the others are reported national data. Returns dataflow ids to use with ilo_get_data / ilo_get_indicator_metadata. Searches the local catalogue only — it does not return statistical values (use ilo_get_data), does not search dimension codes such as countries (use ilo_list_dimension_values) and does not cover non-ILO sources.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoMaximum results (default 20)
queryYesKeywords, matched against dataflow name and id (AND between terms)
offsetNoResults to skip, for pagination (default 0)
provenance_modeNoProvenance verbosity: 'concise' (default — source, url, vintage, retrieval date, citation, license) or 'detailed' (full canonical block with dataset, dimension key and notices)

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
offsetYes
showingYes
has_moreYes
indicatorsYes
provenanceYes
attributionYes
next_offsetNo
total_matchesYes

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?

The description reveals important behavioral details beyond the readOnly/idempotent annotations: AND matching, case-insensitivity, ranking by ILO relevance weight rather than match count, id suffix conventions (_RT, _NB), and the distinction between ILO modelled estimates and reported national data.

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 dense but every sentence contributes: core purpose, query tips, result interpretation, usage with siblings, and exclusions. It front-loads the main action and appends only relevant operational detail.

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?

With a rich output schema present, the description does what is needed: it explains what the search returns, how results are ordered, how to decode ids, and how to route follow-up calls to sibling tools. Nothing essential is missing for correct invocation.

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 the baseline applies. The description adds interpretive guidance about how the query behaves (AND, case-insensitive, relevance ranking) and how to read result ids, but it does not significantly extend the meaning of the individual parameters 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 states a specific verb, 'Search', and a concrete resource, 'the ILOSTAT catalogue of ~1,200 indicator dataflows by keywords in the name or id'. It also explicitly distinguishes itself from its siblings by saying it does not return statistical values, does not search dimension codes, and does not cover non-ILO sources.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

It gives actionable guidance: start with 2–3 English words, drop terms if 0 results, and use the returned dataflow ids with ilo_get_data / ilo_get_indicator_metadata. It also names alternatives for what this tool does not do, such as ilo_list_dimension_values for dimension codes.

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