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store_episodic_memory

Log time-stamped events such as ideas, notes, or tasks, and index them for vector search to build a chronological memory of occurrences.

Instructions

Store an event in episodic memory and index it for vector search.

Logs a time-stamped EVENT (something that happened) and indexes it for vector
search. For a distilled, timeless concept/definition use store_semantic_memory;
for a human-curated palace note use add_memory_entry.

Episodic memory is a chronological log of things that happened — ideas,
notes, papers read, tasks completed, agent runs.

Args:
    content: The text content of the event to remember.
    event_type: One of 'idea', 'note', 'task', 'paper', 'meeting', or
        'agent_run'.
    session_id: Current pipeline session ID (optional).
    metadata: JSON string with extra fields such as title, tags, or source.

Returns:
    A single TextContent confirming the stored event (its row id and type),
    or an error message if the database is missing, fastembed is not
    installed, or the write fails.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
contentYes
event_typeNonote
session_idNo
metadataNo{}

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior5/5

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

No annotations provided, so description carries full burden. It discloses logging time-stamped event, indexing for vector search, return format (TextContent with row id and type), and error conditions (missing database, missing fastembed, write failure).

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?

Well-structured with clear sections: purpose, differentiation, explanation, Args, Returns. Slightly verbose but every sentence adds value. Front-loaded with main action.

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?

Complete description given complexity: explains purpose, usage context, parameter details, return behavior, and error handling. No gaps despite no annotations.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 0%, but description compensates with full Args section describing each parameter: content (text), event_type (with enumerated values), session_id (optional), metadata (JSON string with extra fields). Adds meaning beyond schema.

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 'Store an event in episodic memory and index it for vector search.' It distinguishes from siblings store_semantic_memory and add_memory_entry with specific use-case differentiation.

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?

Explicitly says when to use this tool vs alternatives: 'For a distilled, timeless concept/definition use store_semantic_memory; for a human-curated palace note use add_memory_entry.' Also defines episodic memory as a chronological log of events.

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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