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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions 'machine-readable' but does not explain what happens after submission, whether it's a write operation with side effects, permissions required, or rate limits. For a submission tool, this is a significant transparency 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?

The description is a single sentence that starts with the verb 'Submit' and immediately conveys the tool's scope. Every word contributes meaning; there is no fluff or redundancy. This is exemplary conciseness.

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 14 parameters and no annotation coverage, the description is far too sparse. It fails to explain required parameters, valid feedback_type values, or how to structure the submission. While an output schema exists, it does not compensate for the lack of input guidance. The description is only a purpose statement, not complete enough for correct invocation.

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 description does not explain any parameter semantics. The required feedback_type parameter is never described, and the relationship between the listed feedback subtypes and the numerous optional parameters (suggested_label, current_label, reason, etc.) is unstated. Parameter names provide some hints, but the description adds no meaningful guidance.

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 submits feedback, listing four specific subtypes (label corrections, missing-label suggestions, data-quality reports, general feedback). This distinguishes it from the many data-query sibling tools. The verb 'Submit' and resource 'feedback' are specific and unambiguous.

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 provides clear context for when to use the tool: when submitting machine-readable feedback of the listed types. It implies these scenarios are distinct from querying data, though it doesn't explicitly name alternatives or state when not to use. This meets the 'clear context, no exclusions' level.

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