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memory_save

Save project-specific information like configurations, decisions, errors, and snippets to persistent memory for AI assistants. Organize data by category to maintain context across sessions.

Instructions

メモリを保存する。カテゴリ:

  • config: API URL、ポート、認証情報などの設定

  • dont: やってはいけないこと、過去のミス、ユーザーが怒ったこと、AIのリトライパターン

  • decision: 決定事項、採用した方針

  • log: 実装したこと、解決したエラー

  • snippet: よく使うコマンド、クエリ、便利スクリプト

titleには検索しやすい具体的な名詞を含めること。

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
categoryYesメモリのカテゴリ
titleYes1行の要約タイトル(20文字以内推奨)。検索用の具体的な名詞を含める。例: 「本番API URL」「ログ未読への怒り」
contentYes保存する内容の詳細
tagsNo検索用タグ(オプション、最大5個)
scopeNoスコープ(オプション)。推奨候補: frontend, backend, infra, design, spec, ai, general。自由入力も可
intensityNo怒られ度(オプション、1〜10の整数)。1=提案, 2=軽い注意, 3=明確な指摘, 4=強い不満, 5=激怒・諦め。6以上は手動ピン留め用(数値が大きいほどcontext注入で優先される)。指定時はLLM自動判定より優先される
Behavior3/5

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

With no annotations provided, the description carries the full burden. It successfully discloses behavioral semantics of the intensity parameter (the 1-10 scale meaning from '提案' to '激怒') and category taxonomy, but omits operational details like idempotency, overwrite behavior for duplicate titles, or error conditions.

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?

Perfectly structured with the core action front-loaded, followed by a scannable bulleted category list with inline semantics, and closing with specific title guidance. No redundant text; every sentence earns its place.

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 100% schema coverage and lack of output schema, the description is appropriately complete for a save operation. It comprehensively covers the categorical taxonomy and intensity semantics that the schema cannot express fully, though it could briefly mention whether the operation returns an ID or confirmation.

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?

While the input schema has 100% description coverage (baseline 3), the description adds significant semantic value by detailing what each enum value in 'category' represents (e.g., 'config: API URL、ポート、認証情報などの設定') and expanding the 'intensity' parameter with the full semantic scale of anger levels (1=提案 through 5=激怒).

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 opens with the specific verb+resource 'メモリを保存する' (save memory) and immediately distinguishes this as the creation tool among CRUD siblings by enumerating the five distinct category types (config, dont, decision, log, snippet) that can be saved.

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?

Provides extensive contextual guidance on when to use each category (e.g., 'config' for API URLs/auth, 'dont' for past mistakes, 'snippet' for commands) and specific title formatting instructions. Lacks explicit comparison to sibling tools like memory_update_intensity or memory_delete.

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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