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aiana_memory_feedback

Rate recalled memories to improve future relevance. Provide helpfulness ratings (1, 0, -1) with optional explanations for better search results.

Instructions

Record feedback on a recalled memory to improve future relevance. Rating: 1=helpful, 0=neutral, -1=not helpful.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
memoryIdYesID of the memory being rated.
queryYesThe original query that surfaced this memory.
ratingYesHelpfulness rating: 1=helpful, 0=neutral, -1=not helpful.
reasonNoOptional explanation for the rating.
Behavior2/5

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

With no annotations provided, the description carries full burden for behavioral disclosure. It states the tool's purpose and rating scale but lacks critical behavioral details: whether this is a write operation (implied by 'Record'), what permissions are required, whether feedback is reversible, how it affects future relevance, or any rate limits. The description provides minimal behavioral context beyond the basic operation.

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 extremely concise (two short sentences) with zero wasted words. It's front-loaded with the core purpose and efficiently explains the rating scale. Every sentence earns its place by providing essential information without redundancy or unnecessary elaboration.

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

Completeness2/5

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

For a mutation tool (implied by 'Record') with no annotations and no output schema, the description is incomplete. It lacks information about behavioral implications, error conditions, return values, or how the feedback integrates with the memory system. The description provides basic operation but misses critical context needed for proper tool invocation and understanding of consequences.

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 already documents all parameters thoroughly. The description adds minimal value beyond the schema by mentioning the rating scale (1=helpful, 0=neutral, -1=not helpful), which is already in the schema's enum description. No additional parameter context or usage guidance is provided beyond what's in the structured schema.

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

Purpose4/5

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

The description clearly states the action ('Record feedback') and resource ('on a recalled memory') with the goal 'to improve future relevance'. It distinguishes from siblings like aiana_memory_add or aiana_memory_delete by focusing on feedback rather than creation or deletion. However, it doesn't explicitly differentiate from all siblings (e.g., aiana_memory_search).

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

Usage Guidelines3/5

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

The description implies usage context ('on a recalled memory') and provides rating scale guidance, but doesn't explicitly state when to use this tool versus alternatives like aiana_memory_search or aiana_memory_recall. No exclusions or prerequisites are mentioned, leaving some ambiguity about appropriate usage scenarios.

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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