lesson_recall

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

With no annotations provided, the description fully discloses behavioral traits: it updates the last_recalled timestamp (with clarification that decay is driven by last_reinforced), filters and hides low-confidence lessons, and returns results ranked by confidence. This covers side effects and filtering logic comprehensively.

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 four sentences, each conveying essential information: purpose, filtering rule, behavioral side effect, ranking, and usage guidance. No wasted words; information is front-loaded.

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?

Despite no output schema, the description covers input semantics, filtering logic, side effects, ranking, and usage context. It is sufficiently complete for an agent to understand when and how to use the tool 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%, but the description adds value beyond schema: it explains that min_confidence filters lessons (default 0.2) and that low-confidence ones are archived. It also clarifies query scope (content, context, tags). This enriches parameter understanding without being redundant.

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 searches active lessons by keyword across content, context, and tags. It specifies filtering by confidence and archiving of low-confidence lessons, and ranks results by confidence. This distinguishes it from siblings like lesson_save, lesson_reinforce, and search_history.

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 recommends calling 'at session start to surface relevant patterns before beginning work on a known problem area.' It does not mention when not to use or alternatives, but the recommended usage 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.