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n24q02m

Mnemo - Persistent AI Memory

by n24q02m
OverviewSchemaSourceRelated ServersScoreDiscussionsAdmin
Python

update_memory

When a stored fact or preference changes, modify that memory by ID. Find the memory_id via search, then update its content, tags, or importance.

Instructions

Modify an EXISTING memory by ID. Get memory_id from search results.

ACTION GUIDE — when to use:

  • Use when an existing fact or preference changes. Example: memory_id='abc123', content='User now prefers light mode'

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
tagsNo
sourceNo
contentNo
categoryNo
memory_idYes
importanceNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  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: -"update_memoryOutput"New value: +"update_memoryDictOutput"
  2. Addedv2.1.1

TDQS

A4.2/5.0
Behavior4/5

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

Annotations already indicate readOnlyHint=false (write) and destructiveHint=false (not destructive). The description adds that it modifies an existing memory and requires memory_id, providing useful behavioral context beyond 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 concise, front-loaded, and includes a structured 'ACTION GUIDE' section. Every sentence is necessary and adds value without redundancy.

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?

Despite having an output schema and good guidance on when to use, the description lacks detail on the meaning of optional parameters. For a tool with 6 parameters and 0% schema coverage, this leaves gaps for the agent.

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 description coverage is 0%, but the description only mentions memory_id and content in the example, ignoring other parameters (tags, source, category, importance). It adds minimal meaning beyond the schema structure.

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 modifies an existing memory by ID, distinguishing it from add_memory (create) and delete_memory. It provides an example and specifies that memory_id should come from search results.

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 states 'Use when an existing fact or preference changes' and instructs to get memory_id from search results, giving clear context for when to use this tool over alternatives.

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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