segunda_opiniao

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

With no annotations, the description carries the burden and does reasonably well: it discloses that the model is local and free, that it does not consume Claude tokens, and critically that the answer comes from a small model and should be verified before acting. That reliability caveat is genuinely useful and not inferable from structured fields. It still omits latency expectations and whether the call has any side effects.

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?

Purpose and the token-saving rationale are front-loaded, and the parameter explanations follow in a compact block. The 'ao estilo do zen-mcp-server' reference and some parenthetical asides are expendable, but the text is short and no sentence is redundant.

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?

An output schema exists, so return values need not be described, and the description goes further by warning about output reliability. For a three-parameter advisory tool this covers what an agent needs to invoke it correctly. The main missing piece is sibling differentiation against 'perguntar_local' and the other summarize/ask tools.

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?

Schema description coverage is 0% and the schema declares no enum for 'papel', so the description is doing essential work by enumerating 'codigo' (default, technical review), 'cerebro' (general reasoning), and 'visao' (when the context describes something visual). It also explains 'contexto' as optional relevant code or text. 'pergunta' is left to its self-evident name, which is acceptable.

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 gives a concrete verb+resource: it asks a local model for a second opinion on a decision, code snippet, or plan, and explicitly frames it as a free alternative that avoids Claude token spend. That is a clear, specific purpose an agent can act on. It stops short of a 5 because it never contrasts itself with the nearby sibling 'perguntar_local', which an agent could plausibly confuse with this tool.

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?

It states the triggering situation precisely: use to check a decision, code fragment, or plan before proceeding, without spending Claude tokens. The 'papel' values also route sub-use-cases (technical review vs. general reasoning vs. visual context). It lacks explicit when-not-to-use or a named alternative among the 38 siblings.

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