gpt_verify

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

No annotations are provided, so the description carries the full burden. It discloses the core behaviors (verifying resolution and detecting new issues) and the strictness of verification, but does not explain output format, how unresolved issues are reported, or what 'strict' means operationally.

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 sentences with no filler. The primary purpose is front-loaded, and the secondary behavior is a single clarifying clause. Every word earns its place.

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?

The tool has no output schema and no annotations, so the description should cover what the agent can expect in return. It doesn't state whether the tool returns a verdict, a list of remaining issues, severity levels, or structured data. Inputs are clear, but the missing output expectations create a notable gap.

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% for all three parameters, and their descriptions already define original, revised, and issues_to_check. The description adds no extra parameter-level nuance beyond framing the overall task; baseline 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 verifies that previously identified issues are resolved in revised content, and adds a second distinct behavior of detecting new issues. This is a specific verb+resource combination that inherently distinguishes it from siblings gpt_critique and debate.

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 gives clear context: use it when you have original content, revised content, and a specific list of previously identified issues to verify. It doesn't explicitly name alternatives or state conditions when not to use it, but the usage context is unmistakable.

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