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Glama

Compras del Estado (ARCE)

compras_estado
Read-onlyIdempotent

Query Uruguayan state procurement totals by year, filtered by buyer or item text, to identify top agencies and highest-value purchase items.

Instructions

Cuánto adjudicó el Estado uruguayo en un año (2002 en adelante), en pesos, con los organismos que más compraron y los mayores ítems. organismo filtra por comprador (por ejemplo «Intendencia de Montevideo»); buscar filtra los ítems por texto (por ejemplo «ambulancia»). Las monedas se convierten con la cotización del día. Avisa cuando un solo ítem domina el total: hay registros con cantidad × precio imposibles. La primera consulta de un año baja unos 30 MB y tarda.

Input Schema

NameRequiredDescriptionDefault
anioYes
buscarNo
cantidadNo
organismoNo

Output Schema

NameRequiredDescriptionDefault
anioYes
notaYes
avisoYes
itemsYes
bajadoYes
buscarYes
fuenteYes
organismoYes
organismosYes
sin_monedaYes
total_pesosYes
mayores_itemsYes
sin_convertirYes
adjudicacionesYes
importes_por_monedaYes
excluidas_por_estadoYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.13.2

TDQS

A4.3/5.0
Behavior5/5

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

Annotations already cover safety (readOnly, idempotent, non-destructive), and the description adds substantive traits beyond them: currency conversion at the day's rate, a data-quality caveat about impossible quantity × price records, and an explicit performance cost (first query of a year downloads ~30 MB and is slow). That is exactly the context an agent needs before calling.

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?

Front-loads the core question, then filters, then conversions, then caveats, then cost — a sensible order with little waste. The data-quality sentence is slightly awkwardly spliced in, but every sentence conveys actionable information.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

An output schema exists, so return shape needn't be explained; the description still covers units, scope, filtering, cost, and data reliability. The only real omission is the meaning of `cantidad`, which slightly undercuts completeness for a four-parameter tool.

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 0%, so the description carries the burden: it explains `organismo` and `buscar` with concrete examples, and implies `anio` via the 2002-onward scope. However, `cantidad` (default 10) is never mentioned, leaving one of four parameters undocumented in both schema and description.

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 (how much the Uruguayan State awarded in a year), the units (pesos), the temporal scope (2002 onward), and the shape of the answer (top buying organizations and largest items). No sibling tool covers procurement, and the description makes the domain immediately obvious.

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 clear usage context through worked filter examples («Intendencia de Montevideo» for organismo, «ambulancia» for buscar), which tells the agent how to narrow queries. It stops short of explicit when-not-to-use guidance or naming an alternative, but no sibling overlaps this domain, so the gap is minor.

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