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memory_feedback

Provide feedback on recalled memories to improve future retrieval by marking them as useful or not useful, enabling the system to prioritize relevant information over time.

Instructions

Record whether a recalled memory was useful. This drives the learning flywheel — memories marked useful bubble up, unused ones decay away.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
memory_idYesID from a memory_recall result
usefulYesWas this memory useful?
contextNoWhy was it useful/not useful?
Behavior3/5

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

With no annotations provided, the description carries the full burden. It discloses behavioral traits by explaining the effect on memory prioritization ('memories marked useful bubble up, unused ones decay away'), which adds context beyond basic functionality. However, it lacks details on permissions, rate limits, or error handling.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is appropriately sized and front-loaded, with two concise sentences that directly state the purpose and impact. Every sentence earns its place by providing essential information without waste.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's moderate complexity, no annotations, and no output schema, the description is reasonably complete. It covers purpose and behavioral impact but could improve by addressing usage guidelines more explicitly or detailing response expectations, though it suffices for a feedback tool.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so the schema fully documents the parameters. The description does not add meaning beyond the schema, as it does not explain parameter usage or constraints. Baseline 3 is appropriate since the schema handles the heavy lifting.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose with a specific verb ('Record') and resource ('whether a recalled memory was useful'), and distinguishes it from siblings by focusing on feedback rather than recall, storage, or statistics. It explains the action and its impact on the memory system.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies usage context by mentioning 'recalled memory' and the learning flywheel, suggesting it should be used after memory_recall. However, it does not explicitly state when not to use it or name alternatives like memory_store for storing new memories, leaving some guidance gaps.

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

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