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Glama

Dados Abertos Senado BR MCP

by io.github.SidneyBissoli

Tabelas de gestão de pessoas

senado_pessoal_tabelas
Read-onlyIdempotent

Tabelas de pessoal do Senado conforme o parâmetro tabela. Quantitativos agregados: pessoal (força de trabalho por classe/escolaridade), cargos-funcoes (cargos em comissão e funções de confiança), previsao-aposentadoria, senadores. Listas nominais: estagiarios (ativos), pensionistas, lotacoes (setores), cargos (nomes de cargos). Retorna { tabela, count, total, aviso?, registros[] } — registros agregados (nos quantitativos) ou nominais (nas listas), conforme a tabela, limitados por limite (padrão 100, máx 2000); count 0 e lista vazia quando a tabela não tem registros. O filtro textual opcional casa contra qualquer campo do registro. Para o cadastro nominal de servidores efetivos/comissionados use senado_servidores.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
filtroNoFiltro textual (nome, curso, setor...)
limiteNoMáximo de registros (padrão: 100)
tabelaYesQual tabela de pessoal consultar (quantitativo agregado ou lista nominal)

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

TDQS

A4.9/5.0
Behavior5/5

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

Beyond the annotations (readOnlyHint, idempotentHint, destructiveHint), the description discloses the exact return shape `{ tabela, count, total, aviso?, registros[] }`, the empty-table behavior (count 0, empty list), the limit default/max, and how filtro matches any field. It also notes the aviso field, adding valuable runtime context.

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 earns its place: scope, table categorization, return format, limits, edge cases, filter semantics, and alternative tool routing. It is well-structured with clear punctuation and no fluff.

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?

Despite the tool's complexity (8 table types, mixed aggregate/nominal behavior, optional filter, output schema), the description covers all necessary information: table enumeration, return structure, empty behavior, pagination limits, filter semantics, and sibling routing. An agent has everything needed to invoke it correctly.

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 coverage is 100%, giving a baseline of 3. The description adds meaningful semantics: it explains each tabela value as either aggregate or nominal, defines what each table contains (e.g., 'cargos-funcoes' = comissão/confiança), and clarifies that filtro matches any record field—beyond the schema's generic 'Filtro textual (nome, curso, setor...)'.

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 clearly states a specific verb and resource ('Tabelas de pessoal do Senado conforme o parâmetro tabela') and enumerates all table types with their content. It explicitly distinguishes itself from the sibling senado_servidores, making the tool's scope unambiguous.

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 explicit when-to-use context by separating aggregated quantitative tables from nominal lists, and provides a direct when-not/alternative instruction: 'Para o cadastro nominal de servidores efetivos/comissionados use senado_servidores.' This is clear routing guidance.

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

A4.1/5.0
Disambiguation2/5

Several tools perform overlapping functions (e.g., senado_buscar_materias vs senado_search_processos, senado_contratos vs senado_contratacoes_lista, and three e-cidadania consultation tools). Although descriptions are detailed, an agent can easily select the wrong one without deep inspection.

Naming Consistency3/5

All tools share the 'senado_' prefix, but the pattern is inconsistent: some use Portuguese verbs (buscar, listar, obter), others English 'search', and some are bare nouns (senado_ceaps, senado_mesa, senado_vetos). This mixed convention reduces predictability.

Tool Count2/5

67 tools is far above the 25 threshold, indicating an overgrown surface. While each tool may serve a niche endpoint, the sheer number makes it difficult for an agent to choose efficiently, and many tools could be consolidated.

Completeness4/5

The toolset covers nearly every aspect of Senate open data (legislative, senatorial, committee, administrative, financial, e-cidadania) with list/detail/searches. Minor gaps exist, such as no direct consolidated contract search, but most workflows can be achieved.

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