memory_preference

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

With no annotations provided, the description fully discloses behavioral traits: append-only nature, record mode returns a row with minted id pref-<n>, list mode lists all in append order, audit trail via hash-chained journal, error handling for unknown ids and self-pairs, and advisory nature of responses. All relevant behaviors are transparent.

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 dense but well-structured, starting with the core action and then explaining constraints and modes. Every sentence adds value, though the overall length may be slightly more than minimal. The use of ALL CAPS for key points (e.g., PAIRWISE ONLY, APPEND-ONLY) helps highlight important caveats.

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?

Given the tool's purpose and the absence of an output schema, the description covers all essential aspects: purpose, modes, input requirements, error cases, return values, audit trail, and advisory nature. It is self-contained enough for an AI agent to correctly invoke the tool without additional 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?

Although schema coverage is 100%, the description adds significant context beyond individual parameter descriptions. It clarifies that all four parameters are mandatory together in record mode, that ids must reference existing capsules (with specific error codes), that a self-pair is invalid, and how omission errors are handled. This greatly aids the agent in correct usage.

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 states the tool's function: recording or listing a single pairwise preference-evidence datum. It specifies the verb 'record (or list)' and resource 'pairwise preference-evidence datum'. It explicitly distinguishes from sibling tools by emphasizing 'PAIRWISE ONLY — no score, no ranking, no aggregation beyond the list count' and noting that it is 'EVIDENCE SUBSTRATE' not consumed by recall or ranking.

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 clearly defines two modes (record and list) and explains when to use each. It provides context that the tool is for future use ('nothing in nmemory consumes it yet') and that it is append-only with no update/delete. However, it does not directly compare to sibling tools to explicitly guide the agent on when to use this tool over alternatives.

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