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ILO Labour Statistics (ILOSTAT)

by io.github.SidneyBissoli

Get ILOSTAT indicator metadata

ilo_get_indicator_metadata
Read-onlyIdempotent

Structure of one ILOSTAT dataflow: dimensions (in SDMX key order), their codelists, the time dimension, the source's default selection and the data vintage (last update at the ILO). Use before ilo_get_data to know which filters exist. Does not return statistical values and does not list the codes themselves (use ilo_list_dimension_values).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
dataflowYesDataflow id from ilo_search_indicators (e.g. "DF_UNE_DEAP_SEX_AGE_RT")
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
idYes
nameYes
versionYes
dimensionsYes
provenanceYes
attributionYes
data_vintageYes
time_dimensionYes

Schema Changelog

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

  1. First observed

TDQS

A4.5/5.0
Behavior4/5

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

Annotations already cover the safe, read-only, idempotent nature of the tool. The description adds meaningful behavioral context by disclosing exactly what is returned (structure, not values or codes) and what the vintage represents. This goes beyond the annotations without contradicting them.

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 three sentences with no filler. The core definition is front-loaded, followed by usage timing and explicit exclusions. 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?

For a metadata-retrieval tool with two well-documented parameters, a rich output schema, and safety annotations, the description fully covers what the agent needs: what is returned, what is intentionally excluded, and how it relates to sibling tools. No critical guidance is missing.

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 description coverage is 100%, so the schema already explains both parameters. The description reinforces that 'dataflow' identifies a specific ILOSTAT dataflow and that the tool is a prerequisite for knowing filters, but it does not add new parameter-level details beyond the schema. Baseline 3 is appropriate.

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 names a specific verb and resource: it returns the structure of one ILOSTAT dataflow, including dimensions, codelists, time dimension, default selection, and vintage. It also explicitly differentiates itself from siblings by stating it does not return statistical values and does not list codes, which are handled by other tools.

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?

The description gives clear usage direction: 'Use before ilo_get_data to know which filters exist.' It also names the alternative for code listing: 'use ilo_list_dimension_values,' and states what the tool does not do, preventing misuse. This is explicit when-and-when-not guidance.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

All ILO Labour Statistics (ILOSTAT) toolsInspect
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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.

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