brain_learn

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

With no useful annotation hints (all false), the description carries the behavioral burden. It discloses several non-obvious behaviors: automatic quality evaluation, deduplication, provenance tracking, and supersession. However, it does not explain the consequences of supersession (e.g., whether it modifies or deletes existing memories) or what happens when deduplication triggers. This is a reasonable but incomplete disclosure.

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: purpose, feature summary, and examples. It front-loads the core action and keeps the feature list compact. The examples are useful and relevant, though slightly redundant with the category list.

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?

The description names key behaviors (evaluation, dedup, provenance, supersession) but omits practical details an agent needs: return value or confirmation, failure modes (e.g., low-quality lesson rejected), and the exact side effects of supersedes_id. Given seven parameters, no output schema, and no annotation safety signals, this is a moderate gap.

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 description coverage is 100%, so the schema already documents all seven parameters. The description does not add parameter-level meaning beyond the schema; it only provides example lessons, which are illustrative but not tied to specific parameters. Baseline 3 is appropriate.

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 states a clear action ('Store') and a specific resource ('a new durable lesson, architecture decision, bug root-cause, or team convention'), with concrete examples. It does not explicitly differentiate from sibling tools like brain_recall or brain_forget, but the storage-oriented wording makes the distinction obvious.

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 implies when to use the tool: when you have a lesson, decision, root-cause, or convention to persist. It does not explicitly say when not to use it, nor does it point to alternatives (e.g., use brain_recall to retrieve). Examples help, but exclusion guidance is absent.

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