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

Annotations already declare readOnlyHint=true and destructiveHint=false, so the safety profile is covered. The description adds behavioral value by explaining the result shape (matching memories with id, title, and url), the relationship to fetch, and that Cortex memory persists across other connected AI apps. It does not describe ranking or limit behavior, but that gap is minor given the output schema and low complexity.

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 somewhat long but well structured and front-loaded with the primary use case. Each sentence adds either a usage trigger, an exclusion, a return-behavior note, or context about the memory layer. A few clauses are repetitive, but the content is dense and useful rather than padded.

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 single-parameter read-only search tool with a full output schema and rich annotations, the description is complete. It covers when to use it, when not to use it, what it returns, how it relates to fetch, and the cross-app persistence semantics of Cortex. Nothing an agent needs to select or invoke the tool correctly is missing.

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%, and the query parameter already has a strong description with an example. The tool description reinforces that queries should be in the user's own words and tied to their context, but it does not substantially add parameter-level meaning beyond the schema. 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 clearly identifies search as the tool for retrieving user-specific memories from the Cortex memory layer. It distinguishes itself from fetch by stating that search returns matching memories with id, title, and url, while fetch returns the full text of one memory. The verb and resource are explicit and the boundary with a sibling is drawn.

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 gives detailed, explicit when-to-use guidance: user-dependent facts, preferences, constraints, earlier conversation references, and requests about what the assistant knows or remembers. It also names exclusions: general knowledge, questions not depending on this user, and cases where everything needed is already in the conversation. This is model usage guidance.

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