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

Annotations already cover read-only, idempotent, non-destructive behavior. The description adds valuable epistemic guidance: memories are 'user-provided context, not evidence,' and the current request should override conflicting memory. It also notes the 'accessible brand' constraint, which is useful boundary information beyond the schema.

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 three tight sentences with no redundant phrasing. The core purpose is front-loaded, and each sentence adds distinct value: when to use it, what it returns, and how to interpret the memories.

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 simple one-parameter read tool with a full output schema and comprehensive annotations, the description covers the key operational context: when to invoke it, what it provides, and the reliability caveats. Nothing essential 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 coverage is 100% and the schema itself provides a detailed description, example, and copy rule for brand_id. The tool description does not add meaningful parameter semantics beyond the schema, so the baseline score of 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 the operation: 'Lists this user's durable preferences and business context for that accessible brand.' It specifies a concrete resource (brand memories) and a verb ('lists'), and the scope ('for a selected brand') distinguishes it from sibling tools like list_brands or save_memory.

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 explicit usage context: 'Use once before substantive reporting for a selected brand.' It does not explicitly name when not to use it or alternatives, but the instruction 'Use once' and the focus on pre-reporting context provide clear guidance for the intended workflow.

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