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n24q02m

Mnemo - Persistent AI Memory

memory

Destructive

Store and retrieve persistent AI memories. Use search before adding to avoid duplicates, then update or delete existing memories as needed.

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 Found outdated?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?

The description discloses all actions and their required parameters, including destructive ones like 'delete'. Though annotations already set destructiveHint=true, the description adds rich behavioral context beyond annotations, such as usage patterns and side effects.

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 well-structured with sections for legacy notice, action list, detailed guide, workflow, and proactive use. It is front-loaded with the most important info and each section earns its place, being comprehensive yet efficient.

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 has 22 parameters and many actions, the description covers the main use cases and workflows comprehensively. While it doesn't detail every parameter, it provides enough context for effective use, and output schema exists to cover return values.

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

Parameters4/5

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

With 0% schema description coverage, the description explains many parameters (content, query, memory_id, category, etc.) through examples and requirements. However, some parameters like 'source', 'importance', 'mode', etc. are not described, leaving a gap despite covering the main ones.

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 the tool is a 'Legacy dispatcher for backward compatibility' and lists all actions, effectively communicating its purpose as a unified interface for memory operations. It distinguishes from siblings by advising to 'Use specialized tools' instead.

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 explicitly says to use specialized tools instead, then provides a detailed action guide with examples and a workflow (search -> not found -> add). It covers when to use each action and provides proactive use cases, leaving no ambiguity.

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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