Record closed-book mastery evidence

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

The description adds meaningful non-obvious behavior: mastery only advances under demonstrated, novel, unassisted, closed-book conditions, and acceptance is computed server-side. This goes beyond the sparse annotations and helps the agent predict side effects.

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 tight sentences with the core action first and no filler. Every phrase adds information: what is recorded, when mastery advances, and that the server decides acceptance.

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 tool with eight required parameters, no output schema, and no rich annotations, this description is incomplete. It omits return behavior, how to structure evidence fields, and how this tool relates to sibling learning-state tools, leaving the agent to infer too much.

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?

With 0% schema description coverage, the description must compensate. It maps core criteria like 'demonstrated, novel, unassisted, closed-book' to outcome, novel_prompt, assisted, and closed_book, but it does not explain prompt, response, rationale, or node_id beyond their self-evident names.

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 uses a specific verb ('Record') and specifies the resource ('one verification attempt'), while the title clarifies it concerns closed-book mastery evidence. It clearly distinguishes this from generic state-updating tools like update_node_learning_state.

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 says what the tool does but gives no guidance on when to prefer it over siblings such as update_node_learning_state or append_learning_note. There is no when-not-to-use context or alternative routing.

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