knitbrain_learning_outcome

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

With no annotations provided, the description fully explains the behavioral consequences: 'Useful learnings rise in future recall; ones reported wrong are discredited and sink, and a correction note folds into the lesson'. This discloses the side effects of the tool (updating learning priority and content). It also states the overarching effect ('turns memory from a log into something that compounds'). No contradictions since annotations are absent.

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 sentences with no wasted words. It front-loads the core purpose, then explains behavioral effects and the overall value. Every sentence earns its place by providing essential guidance.

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 tool's simplicity (3 parameters, no output schema), the description covers the purpose, parameter roles, and behavioral impact. It does not specify return values or error handling, but for a reporting tool, this level of completeness is sufficient for correct invocation. It is fully complete for its complexity.

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 description coverage is 67%, but the description adds crucial context: it explains that 'helpful' indicates whether the learning actually helped, and that 'note' is for corrections when wrong. For the 'id' parameter, which lacks schema description, the description implies it refers to the recalled learning's ID. This adds significant meaning beyond the raw schema.

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 purpose: 'Close the loop on a recalled learning: report whether it actually HELPED on this task'. It uses a specific verb ('report') and resource ('learning outcome'), and distinguishes from siblings like knitbrain_record_learning by emphasizing it is for reporting outcomes, not recording new learnings. The description also conveys the impact on future recall, making its role unambiguous.

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 explicitly says to report whether the learning helped on the current task, and that the outcome should be concrete ('not 'noted''). It implies when to use this tool (after applying a recalled learning) and hints at when not to use it (e.g., when no concrete outcome exists). However, it does not explicitly exclude other scenarios or compare directly to siblings like knitbrain_record_false_positive, though the context makes the differentiation clear.

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