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

Given the annotations only indicate non-read-only and non-destructive, the description adds useful behavioral context: the report is read by a human, is always free, and the provider relationship does not gate submission but influences how fast the team acts. It does not detail the exact downstream effect or confirmation, but it provides a clear human-review mental model.

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 key scope is front-loaded in the first sentence, and the later sentences about free access, human review, and provider disclosure each add relevant context. It is slightly chatty with the 'Free, always' phrasing, but the description is not bloated.

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 7-parameter reporting tool with no output schema, the description leaves the expected return or outcome unstated and never distinguishes itself from sibling tools like correct_facts or dispute_finding. The human-reviewed behavior is helpful, but an agent still lacks a full picture of what happens after submission.

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%, so the description does not need to repeat parameter meanings; the examples of wrong website, logo, APIs, count, and score roughly mirror the documented field values. The only additional parameter guidance is 'If you are the provider, say so,' which mostly reinforces the relationship schema entry rather than adding new semantics.

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 what the tool does: 'Report that the catalog has a provider WRONG' and enumerates specific wrong-data cases such as website, logo, APIs, count, and score. It is specific about verb and resource, and the human-read qualifier helps distinguish it from direct-correction tools, though it never names a sibling explicitly.

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 opening sentence effectively says when to use it: when a provider's catalog data is wrong, with concrete examples. It also says it is free and always available, and that declaring provider status only affects priority, which guides the caller. However, it provides no explicit comparison to closely related siblings such as correct_facts or dispute_finding.

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