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record

Capture reasoning and decisions as structured episodes to build memory for AI agents. Records observations, decisions, tensions, questions, and outcomes with context for session-long pattern accumulation and compression.

Instructions

Record a typed episode to memory. Call this when important decisions are made, patterns are noticed, tensions are identified, questions arise, or outcomes are observed. Record the reasoning, not just the fact — 'Chose X because Y' is more valuable than 'using X'. Episodes accumulate during a session and serve as raw material for compression into the continuity file at session end.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
contentYesThe episode content — what happened, what was observed, decided, or questioned.
episode_typeYesEpisode type. observation=pattern/insight, decision=committed choice, tension=conflict/tradeoff, question=open question, outcome=result of action, context=environmental/state info.
sourceNoAgent or source attribution. Defaults to 'agent'.agent
metadataNoOptional JSON metadata to attach to the episode.
Behavior3/5

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

No annotations are provided, so the description carries the full disclosure burden. It successfully explains the accumulation behavior (episodes collect during session) and ultimate destination (compression into continuity file), but lacks operational details like idempotency, memory limits, concurrency, or error handling.

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?

Four sentences with zero waste: sentence 1 defines the action, sentence 2 lists trigger conditions, sentence 3 gives content guidance with concrete example, sentence 4 explains session lifecycle. Every sentence earns its place.

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 100% schema coverage and no annotations/output schema, the description appropriately focuses on systemic context—explaining how episodic recording fits into the broader continuity workflow. Missing only technical constraints (rate limits, max episode size) to achieve completeness.

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?

Input schema has 100% description coverage, establishing baseline 3. The description adds usage context ('Chose X because Y') that informs the content parameter but does not elaborate syntax, formatting rules, or dependencies beyond what the schema already documents.

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 states a specific verb ('Record') and resource ('typed episode to memory'). It clearly distinguishes from siblings by explaining that episodes accumulate during a session and serve as raw material for compression into the continuity file at session end, positioning it distinctly from save_continuity and recall.

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?

Provides explicit positive guidance on when to call ('when important decisions are made, patterns are noticed...') and content quality expectations ('Record the reasoning, not just the fact'). Lacks explicit exclusions or named alternative tools (e.g., when to use recall instead).

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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