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

The description is transparent about read-only behavior, hidden filtering to approved/active memories, ranking logic, and the slower/rate-limited embedding call. This complements the readOnlyHint annotation well.

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 concise and front-loaded with the core purpose, followed by concrete usage timing and operational caveats. Every sentence contributes meaningful information with no fluff.

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 provides enough context for an agent to invoke the tool correctly: what it returns, what filters apply, when to use it, and cost/performance considerations. Since an output schema exists, further return-value detail is not required.

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?

Both parameters are already fully described in the schema, including examples and default behavior for project_id. The tool description adds no parameter-specific information beyond what the schema provides, so the baseline score applies.

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 specific verb and outcome: it recalls/builds task context by returning approved, active memories ranked by relevance and importance. It clearly differentiates itself from mutation-oriented siblings, though it does not explicitly name alternatives like search.

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?

It gives clear when-to-use guidance: call at the START of a task, before touching code. It also notes the OpenAI embedding cost and rate limit, but it does not explicitly state when not to use this tool instead of a sibling such as search.

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