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

No annotations are provided, so the description should disclose behavior. It only mentions sending feedback, without detailing side effects (e.g., storage, notifications), safety (e.g., destructive nature), or prerequisites (e.g., authentication). This under-disclosure leaves uncertainty about the tool's impact.

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 description is a single, well-structured sentence that immediately conveys the tool's purpose. Every word is necessary, and there is no redundancy or extraneous information.

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?

Given the tool has 4 parameters with conditional validation (suggestion required for 'bad' rating) and no output schema, the description lacks details on return values, post-submission behavior, or error handling. This makes it incomplete for a tool with moderate complexity.

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 coverage is 100%, with all parameters described in the input schema. The description adds no additional meaning beyond what is already in the schema, so baseline score 3 is appropriate.

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 clearly states the tool's purpose: sending feedback about translation quality to the VLibras team. It uses a specific verb ('send feedback') and resource ('translation'), and distinguishes it from sibling tools which focus on translation, validation, or auditing rather than user feedback submission.

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 description implies when to use the tool ('when a translation is good or needs correction'), providing clear context. However, it does not explicitly state when not to use it or mention alternative tools among siblings, slightly reducing guidance completeness.

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