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lore_reflect

Reflect on completed sessions by saving summaries and lessons learned; automatically store discoveries as memories for future use.

Instructions

Reflect on a completed session — save what you learned.

Minimal usage: pass session_id and summary. That's enough. The rest are extras for when you discovered something substantial.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
session_idYesUnique session identifier (required).
summaryYesShort summary of what happened in the session (required).
session_dateNoISO date string (e.g. ``"2026-06-02"``). Defaults to today.
topicNoDomain or topic area (e.g. ``"lore_search refactor"``).
task_typeNoOptional category for the session (e.g. ``"build"``, ``"debug"``, ``"review"``, ``"design"``).
what_was_doneNoLonger narrative of the work completed.
decisionsNoKey decisions made, with rationale.
lessons_learntNoList of lessons to propagate to future sessions.
good_patternsNoPatterns that worked well and should be repeated.
user_profile_updatesNoUpdates about the user's preferences or context.
factual_discoveriesNoNew facts to record — stored as bullet text in the reflection. Also auto-inserted as memories when ``auto_insert=True``.
memory_idsNoIDs of existing memories this reflection relates to.
auto_insertNoWhen True (default), automatically inserts each item in ``factual_discoveries`` (score 7.0) and ``lessons_learnt`` (score 8.0) as standalone memories. Duplicate-guarded. Returns created IDs in ``memories_created``.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior4/5

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

No annotations are provided, so the description must disclose behavioral traits. It does so by explaining the auto_insert parameter, which automatically inserts memories with specific scores and duplicate guarding. This is a significant side effect clearly communicated. However, it does not mention other potential behaviors like authorization requirements or rate limits.

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?

The description is extremely concise: two sentences that immediately convey the purpose and minimal usage. Every sentence is essential and well-structured, with no wasted words.

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 13 parameters, 2 required, and the presence of an output schema, the description is reasonably complete. It addresses the core purpose, minimal usage, and the auto_insert behavior. However, it could be improved by noting expected return values (though output schema exists) or conditions for use versus other tools.

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%, with all parameters having descriptions. The tool description adds value by emphasizing minimal usage (session_id and summary) and characterizing other parameters as 'extras', which helps prioritize. But it doesn't add significant new meaning beyond the schema descriptions.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool is for reflecting on a completed session and saving learnings. It uses specific verbs (reflect, save) and resources (session, summary). However, it does not explicitly differentiate from sibling tools like lore_insert or lore_remember, which might also involve saving information. The context of 'session' provides some distinction.

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

Usage Guidelines3/5

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

The description provides clear minimal usage ('pass session_id and summary') and notes that other parameters are extras for substantial discoveries. This implies when to use the tool (after a session) but does not offer guidance on when not to use it or how it compares to alternative tools like lore_insert or lore_remember.

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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