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engram_remember

Store and persist facts, preferences, patterns, decisions, or outcomes across sessions. Automatically redacts secrets, deduplicates by similarity, and supports categorization and retrieval.

Instructions

Store a durable memory (fact/preference/pattern/decision/outcome) that persists across sessions. Every write is scanned for secrets (16+ patterns — OpenAI/Stripe/AWS/GitHub/Slack/Google keys, private keys, connection strings, JWTs): by default detected secrets are redacted to [REDACTED] before storage, or the write is rejected if auto-redaction is disabled. Category and entity are auto-extracted when omitted, a local embedding is generated, and the content is deduplicated against existing memories. Returns: the memory id plus an outcome — "created" (new), "merged" (0.92–0.95 cosine to an existing memory; content/tags/confidence folded into it), or "duplicate" (≥0.95 cosine; not stored unless force:true). Use when you learn something worth remembering about the user, project, setup, or workflow; recall with engram_recall, delete with engram_forget.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
tagsNoOptional string tags for categorization and retrieval, e.g. ["backend", "api"].
forceNoIf true, bypass the duplicate check and store even when a ≥0.95-similar memory already exists (creates a near-identical copy — use sparingly). Default false.
entityNoWhat this memory is about (e.g., "nginx", "deployment", "coding-style", "project-api"). Helps with retrieval.
contentYesThe memory to store. Be specific and factual. Good: "User prefers Fastify over Express for Node.js APIs". Bad: "User likes stuff".
categoryNoType of memory. preference=user likes/dislikes, fact=objective truth about their setup, pattern=recurring workflow, decision=choice they made and why, outcome=result of an actionfact
namespaceNoProject/scope to store under (default "default"). Use a project name to isolate project-specific memories; "default" for general ones.default
confidenceNoHow confident this memory is accurate, 0.0–1.0 (default 0.8). Use 1.0 for facts the user explicitly stated, 0.5–0.7 for inferred preferences.
Behavior5/5

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

With no annotations, the description fully discloses behaviors: secret scanning/redaction, auto-extraction of category and entity, local embedding generation, deduplication logic (three outcomes with cosine thresholds), and force flag behavior. This is comprehensive.

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 relatively long but every sentence adds value. It front-loads the main purpose and includes detailed side effects. Could be slightly more concise but remains efficient.

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?

Despite 7 parameters and no output schema, the description is very complete: explains return type (memory id + outcome), deduplication behavior, secret scanning, and parameter details. No gaps in context.

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 baseline is 3. The description adds meaningful context beyond schema: deduplication thresholds (0.92-0.95, ≥0.95), 'use sparingly' for force, examples for content, and explanation for category enum values. This justifies a 4.

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 clearly states it stores durable memories (facts, preferences, patterns, decisions, outcomes) persisting across sessions, and distinguishes itself from sibling tools engram_recall (retrieval) and engram_forget (deletion). The verb 'store' and resource 'memory' are specific.

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?

Explicit guidance: 'Use when you learn something worth remembering about the user, project, setup, or workflow; recall with engram_recall, delete with engram_forget.' Also mentions auto-extraction and deduplication, providing context for when to use versus alternatives.

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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