lesson_reinforce

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

With no annotations, the description fully discloses side effects: it modifies confidence (with precise magnitude and cap) and can unarchive lessons. It also mentions the return value, making the tool's behavior transparent.

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 concise and well-structured, with each sentence conveying distinct information without redundancy. It efficiently communicates purpose, behavior, usage, and output.

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 the simple operation, the description covers all essential aspects: what the tool does, when to use it, its effects (including quantitative changes and unarchiving), and the return value. No critical information is missing.

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?

The schema already fully describes the single parameter 'id' as 'The lesson ID returned by lesson_save or lesson_recall.' The description does not add additional parameter-specific information beyond what the schema provides, so the baseline of 3 applies.

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's function: reinforcing an existing lesson. It specifies the exact action, the effect on confidence (increase by 0.15, capped at 1.0), and the unarchiving behavior. This distinguishes it from related tools like lesson_save or lesson_recall.

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?

Explicit usage conditions are provided: 'Call this when a lesson recalled via lesson_recall proves relevant to the current task, or when the same mistake recurs.' This gives clear guidance on when to invoke this tool versus others.

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