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

With no annotations, the description carries the behavioral disclosure burden. It does reveal non-obvious behavior: retrieval is embedding-based, no LLM selects results, and multiple raw facts are returned. It does not state read-only status, result ordering, default scoping behavior, or output structure.

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 short sentences with no filler; the retrieval mechanism is front-loaded and the output consequence is stated in the second sentence. Every clause earns its place.

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 nine-parameter tool with no annotations and no output schema, the description gives solid high-level semantics but omits how this tool relates to siblings and how filters/scoping parameters interact. The rich schema compensates partially but cannot supply the missing usage context.

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?

The input schema covers all nine parameters with descriptive text (100% coverage), so the schema carries the heavy lifting; the description adds no parameter-specific meaning. Baseline 3 is appropriate because no gaps in schema coverage exist.

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 ('Search memory') and adds the mechanism 'embedding retrieval without LLM selection', which distinguishes this from LLM-curated recall. It also states the return shape ('multiple matching facts'), though it never names a sibling explicitly.

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 phrase 'without LLM selection' and 'for the agent to digest' imply this tool is appropriate when raw retrieved facts are wanted rather than a synthesized answer. However, there is no explicit when-to-use, prerequisite, or exclusion relative to memory_add, memory_filters, or memory_recall.

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