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Long-Term Memory

Long-Term Memory

by ihwooMil
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
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memory_update

Update an existing memory record with new content or keywords.

Instructions

Update an existing memory's content and/or keywords.

Args: memory_id: The ID of the memory to update. content: New content text (optional, keeps existing if not provided). keywords: New keywords list (optional, keeps existing if not provided).

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "memory_id": "<memory_id>"
    },
    "name": "memory_update"
  }
}

Input Schema

NameRequiredDescriptionDefault
contentNo
keywordsNo
memory_idYes

Output Schema

NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A3.6/5.0
Behavior3/5

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

No annotations are provided, so the description carries the full burden. It discloses that content and keywords are optional and that omitted fields retain their existing values, which is useful. However, it does not mention what happens on invalid memory_id, whether changes are reversible, or any permission requirements. It is adequate but not rich.

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 concise and front-loaded with the core purpose, followed by a simple args list. It avoids unnecessary prose and is easy to scan. The only minor issue is that the args list could be formatted more elegantly, but it is not bloated.

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 tool's low complexity and the presence of an output schema, the description covers the essential behavior and parameters. However, it lacks guidance on when to choose this tool over memory_save or memory_delete, and does not address error cases. It is sufficient for a simple update but could be more complete.

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?

The schema has zero description coverage, so the description must compensate. It clearly explains each parameter: memory_id is the ID, content is new text, keywords is a list, and both optional fields preserve existing values when omitted. This adds meaning beyond the bare schema types and nullability.

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 verb (update), the resource (existing memory), and the specific fields (content and/or keywords). It distinguishes from siblings like memory_save (create) and memory_delete (remove) by focusing on modification of an existing item.

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

Usage Guidelines2/5

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

The description gives no explicit guidance on when to use this tool versus alternatives. It does not mention that it requires an existing memory_id or that creating new memories should use memory_save. The context is implied but not stated, leaving the agent to infer when this is appropriate.

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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