Skip to main content
Glama

Get UNESCO UIS data

uis_get_data
Read-onlyIdempotent

Retrieve UNESCO UIS statistical records filtered by indicators, geo units, and year range. Returns raw data with provenance and optional footnotes, without aggregation or transformation.

Instructions

Statistical records from the UNESCO Institute for Statistics Data API, filtered by indicator codes (from uis_search_indicators, up to 25), geo unit codes (from uis_list_geo_units) and year range. Set include_footnotes for per-record source notes. Returns raw UIS records only — it does not aggregate, convert or otherwise transform values; ILO labour statistics live in the sibling ILOSTAT MCP server. Broad queries are rejected with the record count — narrow by geo unit or years.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
end_yearNoLast year, e.g. 2024
geo_unitsNoGeo unit codes from uis_list_geo_units (e.g. ["BRA","ARG"]); omit for all
indicatorsYesIndicator codes from uis_search_indicators (e.g. ["CR.1"])
start_yearNoFirst year, 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)
include_footnotesNoInclude per-record footnotes (source notes); default false

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
rowsYes
columnsYes
provenanceYes
rows_countYes
attributionYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.4.0

TDQS

A4.4/5.0
Behavior4/5

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

Annotations already declare readOnly/idempotent/openWorld/non-destructive, so the safety profile is covered. The description adds genuinely non-obvious behavior: values are returned raw and untransformed, and oversized queries are rejected with the record count rather than truncated. It stops short of describing pagination or any result-size cap on accepted queries.

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?

Three dense sentences, front-loaded with the resource and filters before the constraints and caveats. Every clause carries information; the only cost is that the raw-records and ILOSTAT disclaimers are stacked into one long sentence, slightly diluting scannability.

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?

An output schema exists, so return values need no explanation. Combined with annotations covering the safety profile and a 100%-documented schema, the description supplies the remaining essentials: where codes come from, the 25-indicator ceiling, raw/unaggregated output, and the rejection behavior for broad queries.

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 description coverage is 100%, so a baseline of 3 applies, and the description goes beyond it by tying 'indicators' to uis_search_indicators with a max of 25 and 'geo_units' to uis_list_geo_units, plus explaining what include_footnotes yields. It adds cross-tool provenance but no syntax detail for year bounds beyond what the schema already shows.

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?

States a specific verb and resource ('statistical records from the UNESCO Institute for Statistics Data API') and immediately scopes it by the three filter axes. It also distinguishes itself from siblings by naming uis_search_indicators and uis_list_geo_units as the code sources, and by ruling out the ILOSTAT MCP server's data.

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?

Gives explicit prerequisites (codes must come from uis_search_indicators / uis_list_geo_units) and operational guidance ('narrow by geo unit or years' when broad queries are rejected). It does not, however, contrast this tool with the generic 'fetch' and 'search' siblings, so routing between those remains implicit.

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