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Mnemo - Persistent AI Memory

by n24q02m
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Python

consolidate_memories

Summarize similar memories within a category to eliminate redundancy and streamline retrieval. Use when a category contains duplicate or closely related entries needing cleanup.

Instructions

Summarize similar memories in a category (requires LLM API keys).

ACTION GUIDE — when to use:

  • Use when a category has too many redundant or closely related memories and needs cleanup. Example: category='preference'

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
categoryYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed4 schema fields changedv2.6.0
    • addedOutput schema / additionalProperties
      Added value: +true
    • removedOutput schema / properties
      Removed value: -{
-  "result": {
-    "title": "Result",
-    "type": "string"
-  }
-}
    • removedOutput schema / required
      Removed value: -[
-  "result"
-]
    • changedOutput schema / title
      Previous value: -"consolidate_memoriesOutput"New value: +"consolidate_memoriesDictOutput"
  2. Addedv2.1.1

TDQS

A3.8/5.0
Behavior3/5

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

Annotations declare readOnlyHint=false and destructiveHint=false, indicating modification but not destruction. The description mentions the requirement for LLM API keys, which is helpful, but does not detail what happens to original memories (e.g., deletion or merging). There is no contradiction with annotations.

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 very concise: two sentences plus a short usage guide. It is front-loaded with the core purpose and contains no unnecessary words.

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?

Given the output schema exists, return values need not be explained. However, the description lacks details about process side effects (e.g., whether original memories are removed or just summarized) and error conditions, which are relevant for a modification tool.

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?

The only parameter 'category' has no description in the schema (0% coverage). The description provides an example usage but does not explain what values are valid or how the parameter is used beyond that.

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 uses specific verb 'Summarize' and resource 'memories in a category', clearly distinguishing from siblings like add_memory or delete_memory. The example further clarifies the purpose.

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

Usage Guidelines4/5

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

The description explicitly states when to use: when a category has too many redundant memories and needs cleanup. It gives a concrete example. However, it does not mention when not to use or compare with other tools.

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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