get_situacao_notas

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

With no annotations, the description carries the burden and does add real value: it discloses the UNEMAT approval thresholds (>=6.0 pass, 4.0–5.9 to final exam with >=75% attendance, 5.0 needed there) and, importantly, the caveat that the course plan may weight units differently, which qualifies the returned result. It does not explicitly state that this is a read-only, non-persistent computation, but the 'get' verb and absence of state changes make that low-risk.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The purpose is front-loaded in the first line, followed by the governing rules — each sentence earns its place since the thresholds directly affect interpretation of the result. The regulatory citation adds a little bulk but supports trust in the numbers.

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

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

For a parameterless computed getter with an output schema already defining the return shape, the description supplies what's missing: the grading rule set and the weighting caveat. Only the relationship to sibling grade tools is absent, which is a minor gap at this complexity level.

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

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

The tool takes zero parameters, so there is nothing for the description to disambiguate; the baseline applies. The description does correctly scope the implicit inputs (current semester, all courses) rather than leaving the agent wondering what data set is used.

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

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

The description states a concrete computed result: 'Quanto precisa tirar nas unidades restantes de cada disciplina do semestre atual' — a specific calculation over the current semester's courses. It is clear what the tool returns, but it never names or contrasts with the obvious sibling get_notas, so an agent must infer the boundary between raw grades and this projection.

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

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

Usage is only implied: the mention of 'unidades restantes' and 'semestre atual' suggests the scenario of planning what grades are still needed, but there is no explicit when-to-use/when-not and no pointer to get_notas or get_avaliacoes as alternatives. An agent can guess the context but is not routed.

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