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Accepts raw conversation history to automatically extract and store useful memories, facts, and decisions using LLM processing.

Instructions

Accept raw conversation messages and automatically extract memories. USE THIS WHEN: you want to dump your recent conversation context so Lore can identify and store useful knowledge (facts, decisions, preferences, lessons). Unlike 'remember' which requires you to decide what to save, this tool accepts raw conversation history and uses LLM processing to extract what's worth keeping. Requires enrichment to be enabled (LORE_ENRICHMENT_ENABLED=true).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
messagesYes
user_idNo
session_idNo
projectNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior3/5

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

No annotations provided; description carries burden. Discloses LLM processing for extraction but does not specify side effects (e.g., message storage, output format, or potential modifications to existing memories). Good but lacks depth.

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 plus usage guideline section. No wasted words; information is front-loaded and clear.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Adequate for core use case but missing details on output, error conditions, or processing guarantees. Output schema exists but its content is not visible to compensate fully.

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

Parameters2/5

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

Schema has 4 parameters with 0% description coverage. Description mentions 'messages' as raw conversation but fails to explain the required structure or the purpose of optional parameters (user_id, session_id, project).

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?

Clearly states it accepts raw conversation messages and extracts memories. Distinguishes from sibling 'remember' by noting it automates extraction.

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 'USE THIS WHEN' for dumping raw conversation context, and contrasts with alternative 'remember'. Also mentions prerequisite (enrichment enabled).

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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