List learned food memories

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

Annotations already declare readOnlyHint=true, idempotentHint=true, destructiveHint=false, and openWorldHint=false, so the safety profile is fully covered structurally. The description adds the meaningful detail that the data is "account-scoped" and "learned," but says nothing about volume, pagination, or freshness of the memories.

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?

A single front-loaded sentence with the verb first and no filler. Every word contributes to scoping the returned data.

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?

With no output schema, the description carries the burden of conveying the return shape; it lists content categories but omits list mechanics such as ordering, count, or whether results are bounded. For a no-arg read tool this is adequate but not complete.

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 tool takes zero parameters, so there is nothing for the description to disambiguate and the baseline of 4 applies. No parameter-level explanation is needed or missing.

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?

States a specific verb ("Read") and a well-scoped resource ("ReceiptCal's account-scoped learned preferences, habits, household facts, portions, cuisines, and dislikes"), so an agent knows exactly what data it returns. It does not, however, distinguish itself from adjacent siblings such as prepare_observe_memory, prepare_edit_memory, or export_memory_document.

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 explicit when-to-use or when-not-to-use statement and no named alternative, but the enumeration of content categories (preferences, habits, household facts, portions, cuisines, dislikes) implicitly tells the agent what kind of question this tool answers versus get_food_profile or list_safety_constraints.

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