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anneal-memory

by phillipclapham
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Python
Hybrid

crystal_recall

Retrieve proven, stable memory patterns relevant to a free-text query or decision. Surfaces graduated wisdom on demand without clogging context.

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 evidence edge). Returns scored patterns (name, level, activation, explanation, tags). In the default 'prompt' mode it is precision-biased: a thin query or no match returns none, by design (surface nothing rather than noise). Pass mode='query' when you are asking explicitly. Durable facts whose cue words appear in the query (or two distinctive words of its text) are listed first, under 'Durable facts matching your words'. max_patterns=0 returns nothing at all, facts included.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
modeNo'query': for a question you are asking on purpose; one keyword is enough, and weaker matches come back too. 'prompt' (default): strict, built for automatic per-turn injection; may return nothing.prompt
queryYesThe free-text query (a prompt, a decision surface, a topic) to find relevant crystallized patterns for.
associativeNoWhen true (default), augment keyword recall with the evidence edge — 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).
max_patternsNoMaximum patterns to surface (precision cap). Default 3.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changedv0.9.41
    • addedInput schema / properties / mode
      Added value: +{
+  "default": "prompt",
+  "description": "'query': for a question you are asking on purpose; one keyword is enough, and weaker matches come back too. 'prompt' (default): strict, built for automatic per-turn injection; may return nothing.",
+  "enum": [
+    "prompt",
+    "query"
+  ],
+  "type": "string"
+}
  2. Changed1 schema field changedv0.9.26
    • changedInput schema / properties / associative / description
      Previous value: -"When 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)."New value: +"When true (default), augment keyword recall with the evidence edge — 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)."
  3. Addedv0.8.2

TDQS

A4.6/5.0
Behavior5/5

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

With no annotations, the description carries the full burden and does so well: it discloses the associative/evidence-edge default, the precision-biased behavior of prompt mode (returns none on thin queries by design), the semantics of mode='query', and the max_patterns=0 edge case that also suppresses durable facts. It even names the return fields, which matters because there is no output schema.

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?

Purpose and scope are front-loaded in the first clause, and every sentence carries substantive information about behavior or routing. It is dense for a four-parameter tool, and the precision-bias theme is restated in both the mode discussion and the max_patterns=0 sentence, which is mild redundancy rather than padding.

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 retrieval tool with no output schema it supplies what is missing elsewhere: the shape of the return (scored patterns with name, level, activation, explanation, tags), the special 'Durable facts matching your words' section, and the empty-result edge case. An agent has everything needed to call it correctly and interpret the response.

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 description coverage is 100%, so the baseline is 3, but the description adds real meaning beyond the schema: it clarifies that mode='query' tolerates a single keyword while 'prompt' is strict, that associative=true is the augmented default versus the pre-0.8.0 keyword-only path, and that max_patterns=0 returns nothing at all, facts included.

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?

States a specific verb (recall) plus resource (crystallized patterns) and goes further by defining what crystallized patterns are — wisdom that graduated out of the always-loaded set into a retrievable store. It distinguishes itself from the sibling crystal_index (the 'always-on menu') in the same breath, so an agent can tell the two apart without opening either schema.

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

Usage Guidelines4/5

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

Gives an explicit trigger condition ('when a decision, design choice, or question touches a topic where prior graduated wisdom might apply') and routes the agent to crystal_index as the complementary menu tool. It also explains the mode split for choosing prompt vs query. It does not, however, distinguish itself from the plain 'recall' sibling or state any when-not-to-use case, so it stops short of full routing guidance.

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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