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sigaa-mcp

sigaa-mcp

by GallegoSz
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
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get_frequencia

Retrieves per-class attendance records, total absences, and attendance percentages for each SIGAA course, with optional filtering by discipline name.

Instructions

Frequência por aula, total de faltas e percentuais de presença de cada turma.

Mais confiável que o campo 'faltas' de get_notas. Consulta cada turma virtual,
então sem filtro pode levar alguns segundos.

Args:
    disciplina: parte do nome da disciplina (sem diferenciar acentos/caixa). Se omitido, todas.

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "get_frequencia"
  }
}

Input Schema

NameRequiredDescriptionDefault
disciplinaNo

Output Schema

NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A4.5/5.0
Behavior4/5

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

With no annotations, the description carries the full burden and does disclose meaningful behavior: it fans out over each virtual class, so latency grows when disciplina is omitted, and its numbers supersede the get_notas 'faltas' field. It does not state what happens when no turma matches the filter or whether results are aggregated across turmas.

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 output shape and the sibling comparison are front-loaded in two short sentences, followed by a compact Args block. No sentence is redundant and the performance caveat sits right where a caller would look for it.

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 values need not be spelled out, and the description still signals what comes back. Purpose, alternative, latency, and the sole parameter are all covered; only edge-case behavior (empty match, multi-turma aggregation) is left implicit.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, so the description must compensate, and it does: disciplina is a partial name match, accent- and case-insensitive, and defaults to all subjects when omitted. That fully specifies the single optional parameter beyond its bare 'string|null' 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?

States a specific verb+resource: attendance per class, total absences, and presence percentages per turma. It also explicitly contrasts itself with get_notas, so an agent can distinguish it from the closest sibling without opening a schema.

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 a clear selection rule: prefer this over the 'faltas' field of get_notas because it is more reliable. It also warns that an unfiltered call queries every virtual class and may take seconds. It does not address the other attendance-adjacent sibling, get_alertas_frequencia, so the routing picture is not fully closed.

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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