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MCP Compras.gov.br

by io.github.opedrosoares

compras_indicadores_por_periodo

Read-onlyIdempotent

Métricas operacionais da API por período (ano/mês).

Endpoint Dados Abertos /modulo-indicadores/2_consultarIndicadoresPorPeriodo. Retorna métricas de USO da API (requisições, latência, downloads), não dados de compras. Útil para análise temporal de disponibilidade do upstream.

Cache 1h.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
anoYesAno de referência dos indicadores (4 dígitos).
mesNoMês (1-12). Se omitido, agrega o ano inteiro. Se informado, filtra apenas o mês especificado.
paginaNoPágina (1-based).
tamanho_paginaNoRegistros por página.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

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

  1. First observed

TDQS

A4.3/5.0
Behavior4/5

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

Annotations already declare readOnly, idempotent, non-destructive behavior. The description adds valuable behavioral context beyond that: the 1-hour cache, the focus on API usage rather than procurement data, and the underlying open-data endpoint, which helps the agent anticipate results.

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 compact and front-loaded: purpose first, then endpoint, then the key disambiguation, then use case and cache behavior. Every sentence delivers distinct information with no filler.

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 read-only, paginated query tool with a rich schema and output schema present, the description covers purpose, endpoint, semantics, exclusions, use case, and caching. Nothing needed for correct selection and invocation 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%, with all four parameters already documented in detail, including the month aggregation behavior. The description adds only a high-level 'period (year/month)' hint, which does not materially improve on the schema.

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 and resource: it returns API usage metrics (requests, latency, downloads) by period, not purchasing data. It also names the exact endpoint and explicitly distinguishes itself from the many purchasing-data tools in the sibling list.

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?

It gives clear context by saying the tool is useful for temporal analysis of upstream availability and explicitly clarifies it is not for purchasing data. However, it does not name an alternative sibling tool or state a when-not-to-use condition beyond the 'not purchasing data' exclusion.

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

A3.6/5.0
Disambiguation3/5

Most tools target distinct resources, and descriptions are extremely detailed, often explicitly warning about look-alikes. However, there is real overlap between composite and single-purpose tools (e.g., compras_checar_sancoes_fornecedor vs compras_perfil_fornecedor_completo vs compras_sancao_*), and similar-looking pairs like compras_contratos_consultar vs compras_contrato_comprasnet_consultar or compras_arp_listar vs compras_pncp_atas_listar require careful reading. With 100 tools, an agent will still face meaningful selection ambiguity.

Naming Consistency3/5

The dominant pattern is snake_case with a compras_ prefix, but the order and style vary: some are domain-first (compras_catmat_buscar), some are verb-first (compras_buscar_contratacoes_similares), and some are bare entity names with no verb (compras_sancao_ceis, compras_pncp_modalidades). The many listar/consultar/buscar variants are readable, but the convention is not predictable enough for a 100-tool surface.

Tool Count1/5

100 tools is an extreme count for any MCP server, regardless of domain breadth. Even if each tool has a legitimate upstream endpoint, this volume will heavily tax context windows and make reliable tool selection harder. Many tools could be consolidated into parameterized families (e.g., contratos, sancoes, pncp resources).

Completeness4/5

The server covers the Brazilian procurement domain remarkably well: catalogs, ARPs, 14.133 contracts, legacy regime, price research, suppliers, sanctions, PGC/PCA, PNCP, and Comprasnet contract subresources. Minor gaps remain, such as listing a supplier's full contract history without specifying an órgão, and some upstream limitations are only papered over with client-side workarounds.

Resources