release_clarification

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

With no annotations, the description carries the full burden. It discloses the decision rule ('matching intent, not deciding generosity') and the selective condition, which is adequate for a simple release action. It could mention irreversibility or output effects, but the core behavioral trait is covered.

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, front-loaded with the action, no redundant phrasing. 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?

For a one-parameter tool with an output schema, the description provides sufficient context (when to use, hownot to misuse) and the schema/annotations fill the rest. It is complete for an agent to select and invoke correctly.

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% (the single parameter 'topic_id' is described as 'Clarification id from available_topics'). The description adds only marginal context ('withheld fact'), so it stays at the baseline of 3.

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 opens with a clear, specific verb ('Release') and resource ('specific withheld fact by id'), immediately distinguishing it from siblings like answer_clarification or reveal_model_answer. The phrase 'when keyword matching missed it' further differentiates it as a fallback mechanism.

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?

Provides explicit 'Use only when' condition, plus an exclusion ('if they didn't ask for it, don't release it'). This gives the agent precise boundaries for when to invoke the tool and what to avoid.

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