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consolidate_reflexions

Extract recurring reflexion themes into semantic memory and prune outdated working memory to maintain relevant context.

Instructions

Distil recurring reflexion themes into semantic memory and prune working memory.

Runs the nightly self-improvement consolidation: every theme an agent raised
>= min_count times in the last `days` becomes a searchable semantic-memory
node (deduped), and working_memory older than 7 days is pruned. Idempotent.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
daysNo
min_countNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior4/5

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

No annotations are provided, so the description bears full burden. It discloses idempotency, deduplication, pruning rules (7-day old working memory), and the algorithm for semantic memory creation. This is fairly transparent, though it doesn't mention permission requirements or the exact nature of pruning (delete vs archive).

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 two sentences, front-loaded with the main action ('Distil... and prune...'), followed by a compact elaboration. Every phrase earns its place; no wasted words.

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 moderate complexity (2 parameters, no nested objects, output schema present), the description covers the core function and parameters adequately. It explains the algorithm and a constant pruning rule. However, it could elaborate on what 'reflexion themes' are and the exact dedup mechanism, but overall it is sufficient.

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?

The input schema has 0% description coverage, so the description compensates by explaining that themes raised >= min_count times in the last `days` become semantic nodes. This gives meaningful context to both parameters beyond their names and defaults. The constant 7-day pruning is also noted, but not parameterized.

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: distilling recurring reflexion themes into semantic memory and pruning working memory. The verb 'distil' and 'prune' are specific, and it distinguishes itself from siblings like 'aggregate_reflexions' by detailing the algorithm with min_count and days, and mentioning dedup and idempotency.

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 mentions it 'Runs the nightly self-improvement consolidation,' implying periodic use, and states idempotency. However, it does not explicitly provide when-to-use or when-not-to-use guidance compared to alternatives like 'store_semantic_memory' or 'consolidate_session_memory'. The context is clear but lacks exclusions.

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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