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engram_feedback

Rate a recalled memory as helpful or unhelpful to fine-tune its future ranking. Each vote updates the memory's feedback score and, after enough votes, adjusts its confidence—closing the learning loop for adaptive recall.

Instructions

Record a helpful/unhelpful vote on a recalled memory to tune future ranking. Updates the memory's aggregated feedback_score (−1 to 1), which feeds the recall score (weight 0.10); after 5+ votes it may auto-adjust the memory's confidence (strongly negative lowers it, strongly positive raises it). Returns: the updated feedback stats for that memory. Call right after acting on a memory from engram_recall to close the learning loop; to remove a bad memory outright, use engram_forget instead.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
contextNoOptional note describing the query or situation that prompted this vote; stored for later review.
helpfulYestrue if the memory was useful in this context (raises its feedback_score and future ranking), false if not (lowers it).
memory_idYesThe id of the memory being rated, taken from a prior engram_recall result.
Behavior5/5

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

No annotations provided, so description fully carries burden. It details the feedback_score range, weight on recall score, confidence auto-adjustment after 5+ votes, and return value.

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?

Single paragraph but well-structured: purpose first, then effects, then usage guidance. Dense with information, no superfluous words.

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

Completeness5/5

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

For a 3-param tool with no output schema, the description fully explains effects, return value, and integration with workflow (recall->feedback). Complete enough for accurate use.

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

Parameters4/5

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

Schema coverage is 100%, baseline 3. Description adds context: explains that 'helpful' raises/lowers score and that memory_id comes from engram_recall. Provides value beyond schema.

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 records a helpful/unhelpful vote on a recalled memory to tune future ranking. It distinguishes from sibling tools like engram_forget, which removes memories.

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

Usage Guidelines5/5

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

The description explicitly instructs when to call it ('right after acting on a memory from engram_recall') and provides an alternative (engram_forget) for removal.

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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