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

With no annotations, the description must carry the behavioral disclosure burden, and it does add useful traits: 'LLM-assisted selection' and 'Returns a concise answer.' However, it does not explicitly state that the operation is read-only, what happens with no matching memory, or behavior differences across modes.

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 two tight sentences with no filler, and the core behavior is front-loaded in the first sentence. Every part is informative and easy to scan.

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?

For a tool with nine parameters, no annotations, no output schema, and a close sibling like memory_search, the description is too thin. It omits output structure, mode distinctions, and the recall-versus-search decision, leaving an agent to infer too much.

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 all nine parameters are already documented in the schema. The description adds no parameter-level meaning, but the schema carries the full burden; 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 uses a specific verb and resource: 'Recall relevant facts' from 'stored memory,' and adds a distinctive mechanism, 'LLM-assisted selection.' It does not explicitly distinguish memory_recall from the sibling memory_search, so it stops short of a 5.

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?

There is no guidance about when to use memory_recall versus memory_search or memory_add. The only sibling reference appears indirectly in the schema ('call memory_filters for the available values'), which is not usage guidance for tool selection.

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