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crystal_recall

Recall proven patterns relevant to a free-text query. Use for decisions, design choices, or questions where prior graduated wisdom may apply. Returns scored patterns with zero noise.

Instructions

Recall crystallized patterns relevant to a free-text query — the on-demand graduated tier (AM-CRYSTAL). Crystallized patterns are proven-and-stable wisdom that graduated OUT of the always-loaded working set into a retrievable store, so a large body of wisdom stays effective without clogging context. Call this when a decision, design choice, or question touches a topic where prior graduated wisdom might apply — recall surfaces the relevant patterns on cue (pair it with crystal_index, the always-on menu of what exists). Associative by default: a pattern grounded in an episode your query matched surfaces even with zero keyword overlap (the Hebbian backend). Returns scored patterns (name, level, activation, explanation, tags); precision-biased — a thin query or no match returns none, by design (surface nothing rather than noise).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
queryYesThe free-text query (a prompt, a decision surface, a topic) to find relevant crystallized patterns for.
max_patternsNoMaximum patterns to surface (precision cap). Default 3.
associativeNoWhen true (default), augment keyword recall with the Hebbian backend — patterns whose evidence cites an episode your query matched surface even with zero keyword overlap. Set false for pure keyword scoring (the pre-0.8.0 path).
Behavior5/5

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

With no annotations, the description fully discloses behavior: associative search via Hebbian backend, precision-biased, return structure (scored patterns with fields). It also notes the default behavior and the zero-match case.

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

Conciseness4/5

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

The description is moderately concise; it front-loads the core purpose and adds necessary detail. Each sentence contributes meaning, though a minor trim could improve brevity.

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?

Given no output schema, the description adequately explains the return format (scored patterns with name, level, etc.) and behavior. For a retrieval tool with complete parameter schema, it is comprehensive.

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%, but the description adds value by explaining the associative parameter's Hebbian backend in detail and mentioning default values. It doesn't merely repeat schema descriptions.

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 explicitly states 'Recall crystallized patterns relevant to a free-text query' with a specific verb and resource. It distinguishes itself from siblings by naming 'crystal_index' and clarifying it is an on-demand tier (AM-CRYSTAL).

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?

It provides clear when-to-use guidance ('Call this when a decision, design choice, or question touches a topic where prior graduated wisdom might apply') and when-not ('a thin query or no match returns none'). It also suggests pairing with crystal_index.

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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