Dismiss Verdict Divergences

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

With no annotations provided, the description carries the full burden and does well: it discloses that dismissal is keyed to the current verdict input hash, that dismissals auto-clear when underlying control or objective changes, and that the model itself is not modified. This goes well beyond a generic 'dismiss' statement, though it does not cover auth/permission requirements.

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 three dense sentences with no filler. The core purpose and key constraint ('WITHOUT changing the model') are front-loaded, and the hash-keyed auto-clear behavior is explained in one well-placed sentence.

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 no annotations and moderate complexity, the description covers what the tool does, when to use it, its non-destructive character, and the important auto-clearing behavior. The provided input schema covers parameter structure and an output schema exists, so return-value explanation is not needed. A small gap is the lack of explicit alternative-tool routing when the structural model is wrong.

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 about 75%: items, reason, and model_id are described, though server_version is not. The description adds useful framing about coverage vs group_sufficiency row kinds, but it does not materially explain parameter formats or semantics beyond what the schema already provides, so a baseline of 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 states a precise verb and resource: 'Dismiss a set of divergences' and clarifies the semantic meaning ('the structural model was right, the LLM was not'). It also explicitly scopes the tool to coverage and group_sufficiency rows and emphasizes that the model is not changed, which helps distinguish it from model-modifying sibling tools.

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 usage context: use for rows that have been reviewed and judged not valid, and for the specific case where the structural model is correct. It does not explicitly name alternative tools or state when not to use this tool, but the guidance is strong enough to orient an agent.

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