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Long-Term Memory

Long-Term Memory

by ihwooMil
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

memory_search

Search stored memories by semantic similarity to a query. Optionally filter by category and specify result count.

Instructions

Search memories by semantic similarity.

Args: query: Search query text (Korean or English). top_k: Number of results to return (default: 5, max: 200). category: Optional category filter (fact/preference/experience/emotion/technical/core_principle).

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": {
      "query": "<query>"
    },
    "name": "memory_search"
  }
}

Input Schema

NameRequiredDescriptionDefault
queryYes
top_kNo
categoryNo

Output Schema

NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A4.1/5.0
Behavior4/5

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

The verb 'search' and the phrase 'by semantic similarity' clearly describe the core read-only behavior, and top_k indicates result count. It does not mention side effects, but the operation is naturally non-destructive and the description is sufficiently transparent for this tool.

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 and well structured, with a short purpose line followed by a compact bullet list of parameters. No filler or redundant information is present.

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?

The description covers the tool's purpose and all parameters, and the output schema is available, so an agent can invoke it correctly. It could optionally mention result ordering or no-result behavior, but these are not critical for basic usage.

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

Parameters5/5

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

All three parameters are explicitly explained: query text language, top_k default and maximum, and category as an optional filter with allowed values. This adds substantial semantic detail beyond the bare schema titles.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the verb 'search' and the resource 'memories', with semantic similarity as the matching method. It is unambiguous on its own, though it does not explicitly contrast with sibling tools like memory_get_related.

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

Usage Guidelines3/5

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

The parameter notes provide useful usage hints such as query language, top_k default/max, and optional category values. However, it does not explicitly state when to prefer this tool over sibling tools like auto_search or memory_get_related; the intended use case is implied rather than stated.

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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