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n24q02m

Mnemo - Persistent AI Memory

memory

Destructive

Store, search, and manage persistent AI memories with actions like add, update, delete, and list. Search before adding to avoid duplicates.

Instructions

Legacy dispatcher for backward compatibility. Use specialized tools (add_memory, search_memory, etc.) instead.

Persistent memory store. Actions: add|search|list|update|delete|export|import|stats|restore|archived|consolidate.

ACTION GUIDE — when to use each:

  • add: Store NEW information. Requires 'content'. Use when saving preferences, decisions, facts for the first time. Example: action='add', content='User prefers dark mode', category='preference', tags=['ui']

  • search: Find existing memories by natural language query. Requires 'query'. Use BEFORE add to avoid duplicates. Example: action='search', query='dark mode preference'

  • update: Modify an EXISTING memory by ID. Requires 'memory_id' (from search/list results). Use when a fact changes. Example: action='update', memory_id='abc123', content='User now prefers light mode'

  • list: Browse all memories, optionally filtered by category. No query needed.

  • delete: Remove a memory by ID. Requires 'memory_id'.

  • stats: Show database statistics (total memories, categories, embedding status).

  • export: Export all memories to JSONL format.

  • import: Import memories from JSONL data. Requires 'data'.

  • archived: List archived memories. Optionally filter by limit.

  • restore: Restore an archived memory by ID. Requires 'memory_id'.

  • consolidate: Summarize and consolidate similar memories in a category using LLM. Requires 'category'.

WORKFLOW: search -> not found? -> add. Found outdated? -> update (with memory_id from results). PROACTIVE: save user preferences, decisions, corrections, project conventions.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
actionYes
contentNo
queryNo
memory_idNo
categoryNo
tagsNo
sourceNo
importanceNo
limitNo
dataNo
modeNomerge
textNo
context_typeNoconversation
autoNo
sinceNo
untilNo
min_importanceNo
include_archivedNo
nameNo
entity_idNo
depthNo
as_ofNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior5/5

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

Annotations already set destructiveHint: true, and the description aligns by detailing destructive actions like delete and update. It adds valuable behavioral context for each action (e.g., add requires 'content', update uses 'memory_id') and notes legacy status, going beyond annotation basics.

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 well-structured: opening disclaimer, action list, detailed guide with examples, workflow, proactive use. It is front-loaded with critical info. Although lengthy due to covering many actions, every sentence contributes value and the structure aids readability.

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 the tool's complexity (22 parameters, many actions), the description covers the core actions thoroughly with examples and workflow. However, it lacks explanation for less common parameters and doesn't mention the output schema (though that exists). It is mostly complete for typical usage.

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 0%, so the description must compensate. It explains key parameters (action, content, query, memory_id, category, tags, data) within the action guide and examples, but many parameters (source, importance, limit, mode, etc.) are not covered. The examples provide partial context, but overall parameter semantics are incomplete.

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 is a 'Legacy dispatcher for backward compatibility' and lists specific actions (add, search, list, etc.). It distinguishes from sibling tools by directing users to 'use specialized tools instead'. The purpose is specific and actionable.

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?

The description provides a detailed ACTION GUIDE for each action with examples, a WORKFLOW (search -> add/update), and proactive usage tips. It explicitly tells when to use this tool vs specialized ones, fulfilling the when/when-not and alternatives criteria.

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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