DESTRAVA — AI Mindset Framework

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

Annotations already cover the safety profile (readOnly, idempotent, closed-world), so the description only needs to add context — and it does: the content is fixed, static conceptual reference material with an official source link, telling the agent not to expect computed or user-specific data. It does not mention caching or versioning of the methodology, a minor remaining gap.

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?

Two short sentences, no filler, with the payload content front-loaded before the scoping constraint. The trailing fragment 'Conceptual content only' is slightly abrupt but carries real disambiguating value.

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?

With no output schema, the description compensates by naming the fields returned and the included source link, and it clarifies the content type. For a single optional language parameter and a closed-world read, an agent has everything needed to call it correctly.

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?

There is one optional parameter (idioma) with 100% schema description coverage including an enum, so the schema fully documents it. The description adds no extra meaning about language selection or fallback behavior, so the baseline 3 applies.

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?

States a specific verb ('Returns') and a precisely bounded resource: the official DESTRAVA 8 AI maturity levels, enumerating the fields returned (names, summaries, blocker, next-leap trigger). It is clearly a different resource than the sibling consultar_indicadores_publicos, but it never explicitly contrasts with it, so it stops just short of a 5.

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?

The clause 'Conceptual content only' implicitly signals this is for qualitative framework content rather than the numeric indicators served by the sibling, which is useful steering. However, there is no explicit when-to-use / when-not-to-use statement and no sibling named as an alternative, so usage must be inferred.

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

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

Annotations already declare readOnly, idempotent, and closed-world behavior, so the safety bar is low. The description still adds meaningful context beyond them: the data is aggregate, carries an official source, and explicitly excludes individual or company data — a privacy constraint an agent would not otherwise know.

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?

A single tightly packed sentence with no filler, leading with what is returned. The parenthetical field list is dense but earns its place by standing in for a missing output schema.

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?

With no output schema, the description compensates by enumerating the return fields (value, unit, sample size, period, instrument versions, limitations). Missing details like result volume or pagination are minor for a parameterless aggregate lookup.

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?

Zero parameters with 100% schema coverage, so the baseline is 4. Nothing in the description conflicts with or needs to compensate for the empty schema.

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?

States a specific verb and resource ('Returns the aggregate indicators already published by DESTRAVA') and even enumerates the returned fields. It does not differentiate itself from the sibling consultar_conhecimento_destrava, so an agent must infer which source holds what.

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?

'already published' implies this is the read path for official published figures rather than raw or knowledge-base data, but no explicit when-to-use, when-not, or alternative is named. The single sibling is never mentioned, leaving selection to inference.

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