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Long-Term Memory

Long-Term Memory

by ihwooMil
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

memory_delete

Delete a memory from the knowledge graph, automatically cleaning up associated edges. Immutable memories are not deletable.

Instructions

Delete a memory from the knowledge graph.

Cannot delete immutable memories. Automatically cleans up graph edges.

Args: memory_id: The ID of the memory to delete.

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_delete"
  }
}

Input Schema

NameRequiredDescriptionDefault
memory_idYes

Output Schema

NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A3.7/5.0
Behavior4/5

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

With no annotations provided, the description takes on the full burden of disclosing behavior. It transparently states two important behaviors: the restriction on deleting immutable memories and the automatic cleanup of graph edges. This is more than a minimal disclosure, though it does not cover all possible edge cases like error handling.

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 short, well-structured, and front-loaded with the main purpose. The additional behavioral notes are presented as separate sentences, making it easy for an agent to quickly grasp the tool's function and constraints without unnecessary verbosity.

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 simplicity of the tool (one parameter, clear action), the description is mostly complete. It covers the action, a key constraint, and a side effect. The presence of an output schema means return values need not be explained. It lacks only details on failure scenarios, which is a minor gap for a delete operation.

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 schema provides no description for the single parameter, so the tool description's explanation ('The ID of the memory to delete') is the only semantic guidance. However, this is essentially a restatement of the parameter name and lacks additional context such as format, origin, or validation rules, which limits its usefulness.

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 action ('Delete a memory') and the resource ('from the knowledge graph'), which unambiguously distinguishes it from sibling tools like memory_update or memory_save. The purpose is explicit and specific.

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 does not provide explicit guidance on when to use this tool over alternatives. It mentions a constraint ('Cannot delete immutable memories') but does not compare with other memory-related tools or describe ideal usage scenarios, leaving the agent to infer the appropriate context.

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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