knowledge_decay

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

No annotations are provided, so the description fully carries the burden. It explains that the tool computes scores based on age, recall frequency, and outcome, and returns a ranked list with visual confidence bars. It implies a read-only operation without destructive side effects, but does not explicitly state idempotency or safety.

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 extremely concise: two sentences plus a visual example. It front-loads the main purpose and uses a metaphor ('old knowledge rots') to make it memorable. Every sentence adds value.

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 parameter set (3 parameters, 1 required) and no output schema, the description fully covers what the tool does, how it works, what the output looks like, and when to use it. No gaps.

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 has 100% description coverage, but the description adds semantic value by explaining that 'show_top' returns entries sorted by lowest confidence first, and that 'min_age_days' defaults to 0 (all). This goes beyond the schema's parameter descriptions.

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 computes a decay score (0–100%) for lessons based on age, recall frequency, and outcome. It specifies the resource (lessons in a Brain) and the action (confidence scoring), distinguishing it from other brain-related tools like brain_predict or brain_doctor.

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 provides a specific use case: 'Use this before a big refactor to know which lessons to trust and which to re-validate.' While it doesn't explicitly exclude other contexts or compare to alternatives, the guidance is clear and actionable.

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