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sigaa-mcp

sigaa-mcp

by GallegoSz
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
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get_alertas_frequencia

Calculates how many absences remain allowed per course before failing for insufficient attendance under UNEMAT's 75% minimum presence rule.

Instructions

Quantas faltas ainda cabem em cada disciplina antes de reprovar por frequência.

Regra da UNEMAT (Resolução 001/2024-CONEPE, Art. 43): presença mínima de 75% da carga
horária presencial; abaixo disso reprova independentemente da nota. Faltas não são abonadas.

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_alertas_frequencia"
  }
}

Input Schema

NameRequiredDescriptionDefault

No arguments

Output Schema

NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A4/5.0
Behavior4/5

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

With no annotations at all, the description carries the full burden, and it does disclose the underlying rule the tool computes against (75% minimum, no abonos, failure regardless of grade). It does not say whether results cover only enrolled/active disciplines or how incomplete course loads are handled, but for a zero-parameter read tool this is solid disclosure.

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?

Two short blocks, output definition first, governing rule second. Every sentence adds information — the regulation citation establishes why the threshold is 75% and that absences are never abonadas, which prevents incorrect agent reasoning.

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?

Zero parameters, an output schema that documents the return shape, and a description that explains the computation rule behind the returned numbers. Nothing an agent needs in order to call this correctly is missing.

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?

The tool takes no parameters, so there is nothing for the description to disambiguate; baseline 4 applies. The schema is empty and consistent with that reading.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The first sentence states the concrete output: how many absences still fit in each discipline before failing by attendance. That is specific enough to separate it from get_frequencia (raw attendance data) or get_notas, though it never names the sibling explicitly.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Usage is only implied: an agent can infer this is the tool for attendance-risk questions rather than raw attendance lookup. There is no explicit when-to-use/when-not or routing statement pointing at get_frequencia or get_situacao_notas.

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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