Recall memories

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

Adds behavioral context beyond annotations: default full-layer search, relevance ranking, and layer tagging. However, the claim that it 'searches every layer by default' conflicts with the schema's layers property, which defaults to 'all but working', creating ambiguity about actual default behavior.

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?

Two tight sentences with no filler. The core action and the usage context are both front-loaded, and every sentence contributes useful information.

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 covers what the tool does, when to use it, and what results look like. The presence of an output schema and comprehensive parameter descriptions reduce the burden further. The main gap is the internally inconsistent default-layer statement, which could mislead an agent into incorrect assumptions.

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 parameters are already well documented. The description does not add meaningful parameter-level semantics beyond what the schema states, and the 'every layer' phrasing could actually mislead relative to the layers parameter default.

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?

States a specific verb and resource ('Search long-term memory') plus a query-based scope. It also clarifies what the tool returns (relevance-ranked results with layer tags), making it readily distinguishable from sibling tools like zenbrain_store or zenbrain_consolidate.

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?

Gives a clear, actionable usage context: 'Use this before answering when the user refers to something from an earlier session.' It does not explicitly list when not to use it or name sibling alternatives, but the guidance is sufficiently clear for an agent to know when to invoke it.

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