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cachly-dev

Cachly — AI Cognitive Brain

memory_crystalize

Preserve AI's learned lessons by compressing recent sessions into a categorized Memory Crystal that survives cleanup and primes future sessions.

Instructions

Compress the last 30-50 sessions and auto-learned lessons into a dense Memory Crystal. A crystal is a compact, structured summary of everything the brain learned — grouped by category (deploy, fix, debug, …). Crystals survive session cleanup and appear in session_start once enough sessions have accumulated. Run this monthly or after a big milestone to preserve institutional knowledge. Returns a digest of what was crystallized.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
instance_idYesUUID of the cache instance
labelNoOptional label for this crystal (e.g. "Q1 2026", "v2 launch"). Auto-generated from date if omitted.
Behavior4/5

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

With no annotations, the description carries the full behavioral burden. It discloses key behaviors: compresses 30-50 sessions, groups by category, creates persistent crystals (survive cleanup), and returns a digest. However, it does not explicitly state whether the operation is read-only or modifies existing sessions.

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 three sentences, front-loads the main action, and contains no wasted words. Every sentence adds value.

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 no output schema, the description adequately explains the tool's purpose, output (digest), and usage context. It could mention error conditions or limits on crystal count, but overall provides sufficient context for an agent to understand the tool's role.

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 coverage is 100%, so baseline is 3. The description adds context for the label parameter with examples and auto-generation behavior, but does not add meaningful detail for instance_id beyond the schema's description.

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 that the tool compresses recent sessions and learned lessons into a Memory Crystal, grouped by category. It distinguishes itself from siblings by noting that crystals survive session cleanup and appear in session_start, but does not explicitly differentiate from similar tools like memory_consolidate.

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 provides explicit guidance: 'Run this monthly or after a big milestone to preserve institutional knowledge.' However, it does not mention when not to use the tool or suggest alternatives, leaving the agent to infer exclusion.

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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