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iai-personal-memory-engine

memory_recall

Read-onlyIdempotent

Recall verbatim decisions, preferences, and prior discussions by natural-language cue before searching a repository. Returns matching hits and anti-hits.

Instructions

Recall verbatim memories by cue — decisions, preferences, prior discussion, rationale. Call before a repository search. Returns hits + anti_hits.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
cueYesNatural-language query to match against stored memories. Embedded server-side via bge-small-en-v1.5 (384d) unless `cue_embedding` is supplied.
languageNoOptional ISO-639-1 language hint for the sleep-suggestion path (8 supported: en/ru/ja/ar/de/fr/es/zh). Defaults to 'en' when omitted. Hot-path retrieval is language-agnostic; this key only affects the sleep-suggestion regex pre-screen.
session_idNoCurrent session id; gets written into every recalled record's provenance. Omit to use '-'.
budget_tokensNoSoft token budget for the response (default 1500). Hits are appended until the next would exceed this budget; at least one hit is always returned.
cue_embeddingNoOptional pre-computed embedding vector for the cue (EMBED_DIM=384 floats; bge-small-en-v1.5). When omitted, the daemon embeds the cue server-side. Used by memory_contradict and tests that need byte-stable embeddings.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
hitsNo
hintsNo
cue_modeNo
anti_hitsNo
budget_usedNo
ann_path_usedNo
pask_teachbackNo
activation_traceNo
overnight_digestNo
patterns_observedNo
exact_authority_usedNo
Behavior4/5

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

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint. The description adds that recall is 'verbatim' and that it 'Returns hits + anti_hits,' providing behavioral expectations beyond the annotations. It does not contradict annotations and adds context.

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 only two sentences, front-loaded with the action ('Recall'), and each clause carries distinct information: what it recalls, when to call it, and what it returns. No filler or redundancy.

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 complete schema, output schema, and rich annotations, the description covers purpose, usage timing, and a return-shape hint. It could mention anti_hits semantics or caveats, but the schema and output schema fill those gaps. It is sufficient for a tool of this complexity.

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?

All 5 parameters are fully described in the schema (100% coverage), so the baseline is 3. The description identifies the 'cue' as the matching mechanism and mentions return behavior, but provides no additional parameter semantics beyond what the schema already offers.

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 uses a specific verb ('Recall') and resource ('memories by cue') and lists content types ('decisions, preferences, prior discussion, rationale'). It distinguishes itself from siblings via 'verbatim' and the 'Call before a repository search' directive, though it doesn't explicitly name alternative tools.

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?

It gives a clear context: 'Call before a repository search.' This tells when to use it but does not explicitly state when not to use it or name alternatives. Per rubric, that's a 4 (clear context, no 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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