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iai-mcp

memory_recall

Read-onlyIdempotent

Recall verbatim memories by providing a natural-language cue. Returns matching hits and anti-hits with valid-from and valid-to timestamps.

Instructions

Recall verbatim memories by cue. Returns hits + anti_hits with derived valid_from/valid_to. Read-only.

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.
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.
session_idNoCurrent session id; gets written into every recalled record's provenance (MEM-05). Omit to use '-'.
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.
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.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
hitsNo
anti_hitsNo
activation_traceNo
budget_usedNo
hintsNo
cue_modeNo
patterns_observedNo
Behavior3/5

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

Annotations already declare read-only, non-destructive, idempotent. Description adds output structure (hits/anti_hits, valid_from/valid_to) but no additional behavioral traits like rate limits or authentication needs.

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?

Two sentences, zero filler. Purpose and key output described in minimal space.

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?

Output schema exists, so return values are covered. Description hits main points. Missing only a note on embedding mechanism, but schema handles that.

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 description coverage is 100%, so description adds no new parameter context. Baseline of 3 is appropriate.

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 verb 'recall', resource 'verbatim memories', and specific output details ('hits + anti_hits with derived valid_from/valid_to'). Distinguishes from siblings by implying simple recall vs structural/mutating alternatives.

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

Usage Guidelines2/5

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

No explicit when-to-use or when-not-to-use. Does not differentiate from memory_recall_structural or other memory tools, leaving the agent to infer context from names.

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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