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これは何ですか?

MCP Light Memory は、コーディングエージェントと MCP クライアント(Warp、OpenCode、JetBrains AI Assistant / PyCharm、Claude Code、Cursor)向けの、軽量でローカルファーストな永続メモリシステムです。これはチェックポイント + 検索レイヤーとして機能し、モデルのコンテキストウィンドウに会話全体を保持することなく、複数セッションにわたる複雑な作業を再開するために必要な最小限の永続状態を保存します。

エージェントがタスクを開始すると、context を呼び出して、関連する過去の決定、落とし穴、制約、仮説を取得します — ランク付けされ、重複排除され、信頼境界で制限されています。タスクが完了すると、作業状態をチェックポイントします。次のセッションでは、再起動後でもメモリはそこにあります。

Related MCP server: mcp-chest-memory

なぜ使うのか?

問題

MCP Light Memory が解決する方法

エージェントがセッション間ですべてを忘れる

Markdown ファイルがディスクに永続化されます。エージェントは BM25 + オプションの埋め込みでそれらを取得します

セッション履歴全体はコンテキストに収まりきらない

関連するメモリのみが取得されます(トークン予算内、MMR で多様化)

クラウド依存 / プライバシー懸念

100% ローカル、オフライン、ネットワーク呼び出しゼロ、デーモンなし

重いセットアップ / 依存関係

実行時依存ゼロ(純 Python 3.8+ stdlib)。オプションで sentence-transformers によりセマンティック検索を向上

保存されたメモリによるプロンプトインジェクション

取得されたメモリはすべて明示的に trust: untrusted のエビデンスとして扱われ、インジェクション警告ヒューリスティック(ADR-015)付き

マルチプロジェクト分離

レジストリ許可リスト、write:false ハードバウンダリ、呼び出しごとのサブプロセス分離を備えたルーター

MCP プロトコルのドリフト

新旧両方の時代に対応:現行の 2026-07-28 + レガシーの 2024-11-05…2025-11-25

仕組み(メカニズム)

  • Markdown がソース・オブ・トゥルースです。 各メモリは YAML frontmatter(id, type, status, tags, sources, links, valid_from, valid_to, supersedes)を持つ .md ファイルです。人間が読め、diff 可能で、永続的です。

  • SQLite は再構築可能なキャッシュです。 BM25/FTS5 インデックス + オプションの埋め込みベクトル + 使用状況トラッキング。これを削除すると、すべて Markdown から再構築されます。

  • 取得: 純 Python BM25 + オプションの dense 埋め込み → RRF 融合 → MMR 多様化 → ポリシーブースト(タイプ/ステータス/時間)→ トークン予算でカット。アダプティブモード:まずスパース、弱い場合のみ dense。

  • ライフサイクル: remember → update → supersede(双方向にリンク、履歴は削除しない)→ forget(アーカイブ、削除しない)→ timeline(時系列ビュー)。過去のクエリは search --at YYYY-MM-DD。

  • 信頼境界: 取得されたコンテンツは === BEGIN/END INTERNAL_RAG MEMORY === で囲まれ、SECURITY NOTICE ヘッダーが付きます。構造化 JSON/MCP は trust: untrusted + オプションの security_flags: ["instruction_like_content"] を保持します。

  • エビデンスの鮮度: 各結果には、ローカルパス状エビデンスの evidence_state(present/missing/unverifiable)が含まれます — 取得時に導出され、永続化されません。

  • マルチプロジェクトルーター: JSON レジストリにより、多数のプロジェクトの前に単一の MCP stdio サーバーを配置します。write:false は子プロセスの起動前に変更系ツールをブロックします。呼び出しごとのサブプロセス分離(共有状態なし)。

セットアップ

前提条件

  • Python 3.8+(py ランチャー、python、または python3 を使用 — インストーラは実際のインタプリタを自動検出し、WindowsApps スタブを拒否します)

  • Git(対象プロジェクトは git リポジトリである必要があります)

  • オプション: より良いセマンティック検索のために pip install sentence-transformers numpy

現在のバージョンは VERSION ファイルで定義されています — 期待値をハードコードせず、それを確認してください(または mlm.py --version を実行)。

クイックスタート

このリポジトリを一度クローンしたら、あとは任意のプロジェクトへインストールします:

# Windows (PowerShell)
git clone https://github.com/PeterPirog/mcp-light-memory.git ~/mcp-light-memory
python ~/mcp-light-memory/install.py . --client warp
# Linux/macOS
git clone https://github.com/PeterPirog/mcp-light-memory.git ~/mcp-light-memory
python3 ~/mcp-light-memory/install.py . --client warp

インストーラは以下を行います:

  • スキルファイルをコピーし、INTERNAL_RAG/ + AGENTS.md を作成します

  • init + checkpoint + validate を実行します(guard が即座に OK になります)

  • 安全にできる場合は MCP サーバーをクライアント設定に自動登録します(または MANUAL_REQUIRED を報告 / JetBrains の手順を表示します)

  • 検証済み Python インタプリタへの絶対パスを書き込みます(Windows の PATH 問題を回避)

python .agents\skills\internal-rag\mlm.py --version   # reports the installed version
python .agents\skills\internal-rag\mlm.py status       # expect: INTERNAL_RAG ready
python .agents\skills\internal-rag\mlm.py guard        # expect: GUARD OK

インストールマトリックス

1つのインストーラ、4つのクライアント、2つの設定スコープ。完全ガイド: docs/INSTALLATION.md。

クライアント

プロジェクトスコープ

グローバルスコープ

Warp(設定書き込みは自動;プロジェクトの有効化は承認が必要な場合があります)

install.py . --client warp

install.py . --client warp --global

OpenCode stable (V1)(安全な JSON 設定書き込みでは自動)

install.py . --client opencode

install.py . --client opencode --global

OpenCode 2 (V2, beta)(安全な JSON 設定書き込みでは自動)

install.py . --client opencode2

install.py . --client opencode2 --global

JetBrains AI / PyCharm(IDE UI で手動)

install.py . --client jetbrains

install.py . --client jetbrains --global

  • --global はクライアント設定のスコープを変更します(~/.warp/.mcp.json と {repo}/.warp/.mcp.json、~/.config/opencode/opencode.json とプロジェクトの opencode.json)。サーバーは依然としてインストール先の対象プロジェクトを指します。

  • 多数のリポジトリに対して1つのグローバル MCP エンドポイントが必要ですか? マルチプロジェクトルーターを使用してください — docs/MCP-MULTI-PROJECT.md。

  • JetBrains/PyCharm は支援のみで、完全自動ではありません: インストーラが JSON + Working Directory を準備します。Settings → Tools → AI Assistant → MCP でサーバーを追加し、Server level = Project または Global を選択します。

  • クライアントごとの手動セットアップ(インストーラなし): docs/INSTALLATION.md + クライアントページ(Warp ・ OpenCode)。

ゼロショット: Warp と OpenCode 向けのコピーペーストプロンプト

これらのいずれかをクライアントエージェントに直接貼り付けることができます。C:\Projects\App を実際の対象リポジトリパスに置き換えてください。

Warp — 1つのプロジェクトへのインストール:

Install and configure MCP Light Memory (mcp-light-memory) as an MCP server for project C:\Projects\App in Warp, using project scope. Use the repository https://github.com/PeterPirog/mcp-light-memory. If the tool is not cloned yet, clone it to a stable location outside the project; if it already exists, update it with git pull --ff-only. Apply the canonical installation contract from the repository and run install.py with TARGET_PROJECT=C:\Projects\App and --client warp without --global. Do not force-overwrite an existing configuration. After installation, verify from cwd=C:\Projects\App: mlm.py --version, mlm.py status, and mlm.py guard, and confirm that the Warp configuration contains mcp-light-memory and the C:\Projects\App path. Report success only after MCP REGISTRATION: REGISTERED and successful verification. If Warp requires an additional project activation/toggle/approval, state the exact client-side step and do not claim the server is active before it is completed.

Warp — 1つのプロジェクトのグローバルクライアント設定:

Install and configure MCP Light Memory (mcp-light-memory) in Warp globally for project C:\Projects\App. Use the repository https://github.com/PeterPirog/mcp-light-memory. If the tool is not cloned yet, clone it to a stable location outside the project; if it already exists, run git pull --ff-only. Apply the canonical installation contract and run install.py with TARGET_PROJECT=C:\Projects\App, --client warp, and --global. Remember: --global means the global Warp client configuration, while the server must still be bound to C:\Projects\App; do not use the multi-project router. After installation, verify from cwd=C:\Projects\App: mlm.py --version, mlm.py status, and mlm.py guard, and confirm that the global Warp configuration contains mcp-light-memory and the C:\Projects\App path. Report success only after MCP REGISTRATION: REGISTERED and successful verification.

OpenCode — 1つのプロジェクトへのインストール(stable/V1):

Install and configure MCP Light Memory (mcp-light-memory) as an MCP server for project C:\Projects\App in OpenCode. By "OpenCode" I mean stable/V1, so use --client opencode, not opencode2. Use the repository https://github.com/PeterPirog/mcp-light-memory. If the tool is not cloned yet, clone it to a stable location outside the project; if it already exists, run git pull --ff-only. Run install.py with TARGET_PROJECT=C:\Projects\App and --client opencode without --global. Do not force-overwrite an existing configuration. If the installer returns MCP REGISTRATION: MANUAL_REQUIRED (for example because opencode.jsonc exists), do not report success: safely edit the JSONC while preserving comments and unrelated settings if you have appropriate file-editing tools; otherwise report the exact manual action required. After real registration, verify from cwd=C:\Projects\App: mlm.py --version, mlm.py status, and mlm.py guard, and confirm that the OpenCode configuration contains mcp-light-memory and C:\Projects\App.

OpenCode — 1つのプロジェクトのグローバルクライアント設定(stable/V1):

Install and configure MCP Light Memory (mcp-light-memory) globally in OpenCode for project C:\Projects\App. By "OpenCode" I mean stable/V1, so use --client opencode. Use the repository https://github.com/PeterPirog/mcp-light-memory. If the tool is not cloned yet, clone it to a stable location outside the project; if it already exists, run git pull --ff-only. Run install.py with TARGET_PROJECT=C:\Projects\App, --client opencode, and --global. --global means the global OpenCode client configuration, while the server must still be bound only to C:\Projects\App; do not use the multi-project router. If the installer returns MCP REGISTRATION: MANUAL_REQUIRED, do not report success and follow the safe JSONC instructions. After real registration, verify from cwd=C:\Projects\App: mlm.py --version, mlm.py status, and mlm.py guard, and confirm that the global OpenCode configuration contains mcp-light-memory and the C:\Projects\App path.

OpenCode 2 / V2 の場合は、同じプロンプトを使用し、OpenCode 2 / V2 と明示的に指定して、--client opencode2 を要求してください。他のバリエーション: docs/ZERO-SHOT-SETUP-PROMPTS.md。


設定の詳細

Warp

Warp は MCP サーバー設定を ~/.warp/.mcp.json(グローバル、自動起動)または {repo}/.warp/.mcp.json(プロジェクト、Warp ドキュメントに従って手動トグルが必要)から読み取ります。形式: mcpServers.<name> に command、args、working_directory を指定します(常に設定してください — メモリストアはここから解決されます)。examples/warp.example.json と docs/WARP-SETUP.md を参照。

OpenCode stable (V1)

OpenCode はプロジェクトルートの opencode.json/.jsonc、またはグローバルの ~/.config/opencode/opencode.json を読み取ります。V1 サーバーは mcp.<name> の下にフラットに配置され(servers サブキーなし)、enabled: true と command を配列として持ちます — examples/opencode-legacy.example.json と docs/OPENCODE.md を参照。

OpenCode 2 (V2, beta)

設定ファイルは同じですが、形状が異なります: mcp.servers.<name>、command は配列、enabled フィールドはありません(V2 は disabled: true で無効化します)— examples/opencode-v2.example.jsonc と docs/OPENCODE.md を参照。

JetBrains AI Assistant / PyCharm

PyCharm は MCP 設定ファイルを自動読み取りしません。インストーラは貼り付け可能な JSON + Working Directory を表示します。Settings → Tools → AI Assistant → MCP (STDIO) でサーバーを追加し、Server level = Project または Global を選択します。examples/jetbrains.example.json を参照。


マルチプロジェクトルーター

多数のプロジェクトの前に1つの MCP 接続 — レジストリ許可リスト、write:false のハードバウンダリ、呼び出しごとのサブプロセス分離。

レジストリファイル(projects.json)

{
  "projects": {
    "backend": { "root": "/abs/path/backend", "write": true },
    "shared-lib": { "root": "/abs/path/shared-lib", "write": false }
  }
}

ルーター用 Warp 設定

{
  "mcpServers": {
    "mcp-light-memory-router": {
      "command": "python3",
      "args": ["/abs/path/mcp-light-memory/.agents/skills/internal-rag/irag_mcp_router.py", "--registry", "/abs/path/projects.json"],
      "working_directory": "/abs/path/mcp-light-memory"
    }
  }
}

詳細は docs/MCP-MULTI-PROJECT.md を参照。


ワークフロー

context --task "current task"
  ↓
recovery, if required (RECOVERY REQUIRED)
  ↓
checkpoint before first change
  ↓
implementation
  ↓
checkpoint after each milestone
  ↓
guard before finishing

主要コマンド(CLI エイリアス: mlm.py またはレガシーの irag.py):

mlm.py context --task "..."
mlm.py checkpoint --reason "..."
mlm.py search --query "..." --limit 8
mlm.py remember --type decision --title "..." --body "..."
mlm.py show <ref>
mlm.py update <ref> --status superseded
mlm.py status
mlm.py guard
mlm.py validate
mlm.py doctor

パス対応(リブランド: internal-rag → MCP Light Memory)

新しい名前

レガシーパス(互換性のため保持)

MCP Light Memory(製品)

internal-rag(非推奨の製品名)

mlm / mlm.py(主要 CLI)

irag.py(レガシーエイリアス、引き続き動作)

mcp-light-memory(MCP サーバー名)

internal-rag(レガシー、設定で引き続き動作)

mcp-light-memory-router(ルーター名)

internal-rag-router(レガシー)

INTERNAL_RAG/(ストレージフォルダ — 変更なし)

—

.agents/skills/internal-rag/(スキルディレクトリ — 変更なし)

—

ディスク上のフォルダ INTERNAL_RAG/ とスキルディレクトリ .agents/skills/internal-rag/ は、ゼロマイグレーションの後方互換性のために意図的にレガシー名のままにされています。docs/MIGRATION-TO-MCP-LIGHT-MEMORY.md を参照。

永続メモリ(CRUD)

remember --type decision --title "..." --body "..." --tags "a,b" --evidence "src/x.py:42" --links "decisions/other.md"
show <path-or-id>
show <ref> --section Knowledge
update <ref> --add-tags "new" --append "New evidence: ..."
supersede <ref> --by <new> --reason "..."
forget <ref>              # archives, does not delete
link --from <ref> --to <ref>
timeline --limit 20
status
history

タイプ: decision, knowledge, constraint, gotcha, failure, hypothesis, session。

タスクスタック(割り込み)

mlm.py push --task "interrupted work" --reason "user-priority"
mlm.py tasks
mlm.py resume
mlm.py forget-task <id>   # drop a specific task
mlm.py forget-task         # clear the whole stack

設定(.irag.yml、オプション)

retrieval:
  limit: 10
  mmr_lambda: 0.4
  min_score: 0.3
  embeddings: auto        # auto | on | off
  profile: english-fast   # english-fast (default) | multilingual (PL/EN projects)
  embeddings_model: null  # explicit model overrides the profile
tokens:
  context_budget: 5000
checkpoints:
  auto_archive_sessions: true
  max_task_stack: 24

mlm.py config で有効な設定を表示します。mlm.py config --init でテンプレートを書き出します。

オプションの埋め込み(より良い取得)

pip install -r requirements-optional.txt

パッケージが利用可能で、.irag.yml に embeddings: auto(デフォルト)が設定されている場合、取得は BM25 へのフォールバック付きで埋め込みを使用します。実行時に --embeddings on|off|auto で上書きできます。

2つの取得プロファイル(docs/EMBEDDINGS.md を参照):

  • english-fast(デフォルト、all-MiniLM-L6-v2)

  • multilingual(intfloat/multilingual-e5-small)— ポーランド語と英語のプロジェクト向け

オフライン / エアギャップ

python pack.py --with-embeddings --profile english-fast
# -> internal-rag-offline-1.8.1.zip   (name from pack.py; 1.8.1 = VERSION file)
# On the air-gapped machine:
unzip internal-rag-offline-*.zip -d internal-rag-offline
pip install --no-index --find-links wheels/ -r requirements-optional.txt
python install.py "/path/to/project" --client <warp|opencode|opencode2|jetbrains>

詳細は docs/OFFLINE.md を参照。

プライバシーと Git

デフォルトのインストールモードはローカルのみです。インストーラはプロジェクトの .gitignore ではなく .git/info/exclude を使用するため、ローカルメモリと統合ファイルが誤ってコミットされることはありません。

プロジェクトを公開する前に:

python .\privacy_check.py "D:\path\to\project"

期待される結果: RESULT: PASS

プロジェクトからの完全な削除

python .\uninstall.py "D:\path\to\project"

アンインストーラはリポジトリ外にバックアップを作成し、その後 INTERNAL_RAG とその統合を削除します。メモリデータを保持するには --keep-memory を使用します。

ドキュメント

対象プロジェクト内の構造

project/
├── AGENTS.md
├── .irag.yml                    # optional config
├── INTERNAL_RAG/
│   ├── WORKING_STATE.md
│   ├── INDEX.md
│   ├── .checkpoint.json
│   ├── decisions/  knowledge/  gotchas/  failures/  hypotheses/  sessions/  archive/
│   └── exports/
├── .agents/skills/internal-rag/
│   ├── SKILL.md
│   ├── mlm.py                   # primary CLI (forwards to irag.py)
│   ├── irag.py                  # core (legacy alias, still the canonical module)
│   ├── irag_embeddings.py       # optional plugin
│   └── irag_hooks.py            # optional git hooks
└── .opencode/                   # OpenCode integration (optional)

真実の源

  1. 現在のユーザー指示、2. 現在のコード/テスト/設定、3. 仕様/ADR、4. 検証済みメモリ、5. セッションノート、6. 仮説。

メモリは古くなっている可能性があります。コードが優先されます。

ライセンス

MIT.


変更履歴

1.8.0 — JetBrains 手動セットアップ

  • --client jetbrains はダミーの設定ファイルを書き込まなくなりました (PyCharm は MCP 設定ファイルを無視します)。代わりに、貼り付け可能な JSON と IDE メニューの手順を出力します。

  • --unregister --client jetbrains は IDE UI での削除を促すリマインダーを出力します。

1.7.2 — JetBrains cwd + クライアント別メッセージ

  • JetBrains: working_directory をヒントとして書き込み、Settings → Tools → AI Assistant → MCP に設定する正確なパスとともに WARNING を出力します。

  • クライアント別の再起動メッセージ (Restart PyCharm / Restart Warp / Restart OpenCode)。

  • インストール出力に Memory store: <path> を表示して、即座に検証できるようにします。

1.7.1 — Windows Python スタブ修正

  • detect_python() は WindowsApps の 0 バイトスタブを拒否し、py -0p を優先し、各候補を --version で検証します。

  • 登録後の検証: 設定を書き込んだ直後に --version を実行し、PASS/FAIL を報告します。

  • --unregister は空の設定ファイルと親ディレクトリを削除します (無効な .warp/.mcp.json スケルトン → GUARD STALE を修正)。

1.7.0 — MCP Light Memory へのリブランド

  • internal-rag から MCP Light Memory (mcp-light-memory) への全面リブランド。新しい CLI エイリアス mlm (mlm.py)。ロゴ/アイコンアセット。移行ドキュメント。GitHub リブランドチェックリスト。

  • 後方互換: irag.py、INTERNAL_RAG/、旧 MCP サーバー名を非推奨エイリアスとして保持。

  • リブランド一貫性テスト 18 件。

1.6.1 — v1.6 後の堅牢化

  • ミューテーション/ライフサイクルベンチマーク (11 シナリオ)。信頼境界 (ADR-015): trust: untrusted + security_flags。エビデンスの鮮度 (ADR-016): evidence_state。スケールベンチマーク (100/1k/10k)。ルーターセキュリティリグレッション (+12 テスト)。ドキュメント一貫性テスト。249 テストが合格。

1.6.0 — 検索品質 + MCP 2026-07-28

  • メモリ品質ベンチマーク (37 ケース)。MCP 2026-07-28 デュアル世代 (server/discover、_meta、structuredContent、outputSchema)。レジストリの厳格な write。チャンクプレフィックス内のソース。適応型検索。リンクを考慮したコンテキスト。consolidate --prepare。ルーター遅延ベンチマーク。ADR-010…016。

1.5.0 — 棄権ゲート + マルチプロジェクトルーター

  • 関連性/棄権ゲート (--meta)。FTS5 候補プリフィルター。マルチプロジェクト MCP ルーター。MCP プロトコルの堅牢化 (stdout のみ、SDK 検証済み)。168 テスト。

1.4.0 — チャンキング + 重複排除 + 時間的ライフサイクル

  • セクション認識チャンキング (スキーマ v3)。SimHash 重複排除。多言語 PL/EN プロファイル。時間的ライフサイクル (valid_from/valid_to/supersedes/--at)。consolidate --dry-run。

1.3.0 — 永続エンベディングキャッシュ

  • SQLite 内のチャンク単位 float32 BLOB。複数モデルの共存。index --vacuum/--embed-missing。

1.0.2 — トークン予算 + プライバシー

  • トークン予算の強制。古いメモリの検出。重複検出。書き込み時のプライバシースキャン。自動チェックポイントタイマー。オフライン/エアギャップパック。

1.0.0 — 初回リリース

  • BM25 + MMR 検索。メモリの完全な CRUD。タスクスタック。MCP サーバー (JSON-RPC stdio)。Git フック。診断。エクスポート/インポート。トークン予算。

Available Tools

8 tools
checkpointSave Task CheckpointA

Persist current operational task state so work can be resumed after interruption or context loss. Use at meaningful milestones and before ending a work session; use remember for reusable knowledge. This writes local checkpoint/task state, does not delete durable memories, and performs no network access.

NameRequiredDescriptionDefault
nextNoRecommended next action when work resumes.
phaseNoCurrent task phase or milestone name.
reasonYesWhy this checkpoint is being created.
blockersNoKnown blockers or unresolved issues.
completedNoWork completed since the previous checkpoint.
in_progressNoWork currently underway.

TDQS

A4.4/5.0
Behavior4/5

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

Annotations already declare the safety profile (readOnly=false, destructive=false, openWorld=false), and the description adds the local-write scope, confirms no durable-memory deletion, and states no network access. It does not clarify whether checkpoints overwrite prior state or how the non-idempotent behavior manifests, so it is strong but not fully exhaustive.

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?

Two sentences, front-loaded with the core action, then usage and behavioral caveats. Every clause earns its place with no redundancy.

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?

With no output schema, the description adequately covers when to call it, what state it writes, and what it does not touch, which is sufficient for a checkpoint write. It could be slightly more complete on resume interaction with the sibling 'resume' tool.

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

Parameters3/5

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

Schema description coverage is 100%, so all six parameters (reason, next, phase, blockers, completed, in_progress) are already documented in the schema. The description adds no parameter-level detail beyond what the structured fields provide, making the baseline 3 appropriate.

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?

States a specific verb and resource ('Persist current operational task state') with the added purpose of resuming after interruption or context loss. It explicitly distinguishes itself from the sibling 'remember' by noting that tool is for reusable knowledge, so an agent can tell them apart without opening schemas.

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?

Gives explicit when-to-use guidance ('at meaningful milestones and before ending a work session') and names the alternative ('use remember for reusable knowledge') with the condition that selects it. Nothing is left to inference about timing or routing.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

contextLoad Task ContextA

Start or resume a task by retrieving relevant durable project memories and current working state. Use before project changes or after a context reset; use search for a focused lookup. This call may update task or usage state, is non-idempotent, does not delete durable memories, and performs no network access.

NameRequiredDescriptionDefault
taskYesShort description of the task being started or resumed; used to retrieve relevant project context.
limitNoMaximum number of relevant memories to include in the context packet.

TDQS

A4.4/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=false, idempotentHint=false, openWorldHint=false, and destructiveHint=false, so part of the description ('non-idempotent', 'no network access') restates structured data. It does add value beyond them by disclosing what state is touched ('may update task or usage state') and a non-obvious negative guarantee ('does not delete durable memories').

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?

Two sentences, no waste, with the core action front-loaded and usage/behavioral caveats packed into the second sentence. Every clause carries information.

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?

For a 2-parameter, non-destructive mutation with full annotation coverage and no output schema, the description covers trigger, alternative, state mutation, idempotency, and network behavior. It stops short of describing what the returned 'context packet' contains, a minor gap given no output schema is declared.

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

Parameters3/5

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

Schema description coverage is 100%, so both 'task' and 'limit' are already documented in the schema. The description adds no syntax, format, or defaulting detail beyond that, so the baseline of 3 applies.

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?

States a specific verb ('Start or resume a task') and resource ('durable project memories and current working state'), and explicitly routes the focused-lookup case to the sibling 'search'. An agent can distinguish it from search, resume, and checkpoint without opening any schema.

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?

Gives concrete triggers ('before project changes or after a context reset') and names the alternative tool and the condition that selects it ('use search for a focused lookup'). Nothing about when-to-use is left to inference.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

guardCheck Checkpoint FreshnessA
Read-onlyIdempotent

Check whether project state changed since the last checkpoint. Use before finishing a task to detect uncheckpointed work, then checkpoint if stale. This operation is read-only and idempotent, does not modify memory or task state, and performs no network access.

NameRequiredDescriptionDefault

No parameters

Output Schema

NameRequiredDescription
okYesTrue when project state is consistent with the last checkpoint.
fingerprintNoFingerprint representing the current project state used by the guard check.
changed_filesNoProject files detected as changed since the checkpoint baseline.

TDQS

A4.4/5.0
Behavior3/5

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

Annotations already declare readOnlyHint=true, idempotentHint=true, destructiveHint=false, and openWorldHint=false, and the description largely restates these facts ('read-only and idempotent', 'no network access'). It adds only marginal new context via 'does not modify memory or task state', which is implied by readOnlyHint. With annotations carrying the safety profile, this is adequate but adds little.

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?

Three short sentences, with the core purpose and the usage trigger front-loaded before the safety caveat. The final sentence is largely redundant with the annotations, which slightly dilutes the otherwise tight structure.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a zero-parameter, read-only check with an output schema present, the description covers purpose, when to call it, what to do with the result, and the safety profile. Return-value semantics are left to the output schema, which is appropriate.

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 tool takes zero parameters, so there is nothing for the description to disambiguate; the baseline of 4 applies. No parameter-related claims are made or needed.

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?

States a specific verb (check) and resource (whether project state changed since the last checkpoint), which is a distinct operation from the sibling 'checkpoint'. The name 'guard' is opaque on its own, but the description resolves it immediately.

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 names the trigger ('Use before finishing a task to detect uncheckpointed work') and the conditional follow-up action ('then checkpoint if stale'), routing the agent to the sibling tool under a stated condition. Nothing is left to inference.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

rememberStore Durable MemoryA

Store durable project knowledge that should survive across sessions. Use for stable decisions, constraints, gotchas, failures, hypotheses, or reusable knowledge; use checkpoint for temporary task progress. This writes local durable memory, does not delete existing memories, and performs no network access.

NameRequiredDescriptionDefault
bodyYesDurable content to preserve: a decision, fact, constraint, gotcha, failure, hypothesis, or reusable session knowledge.
tagsNoOptional comma-separated tags used to organize or retrieve the memory.
typeYesMemory category describing the kind of durable knowledge being stored.
scopeNoOptional scope describing where this memory applies.
titleYesShort, specific title for the memory.
statusNoMemory confidence state: active for established knowledge or tentative for information that still needs confirmation.
evidenceNoOptional source or project-relative evidence reference supporting the memory.
consequenceNoOptional impact or consequence of this memory for future work.

TDQS

A4.2/5.0
Behavior3/5

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

The description's behavioral claims ('does not delete existing memories', 'performs no network access') largely restate destructiveHint=false and openWorldHint=false from the annotations, and it adds the useful locality framing of 'local durable memory'. It does not disclose what the annotations cannot: whether repeated calls with identical content create duplicates (idempotentHint=false implies they do) or what the write returns. With annotations already carrying the safety profile, this is adequate but adds limited new context.

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?

Three short sentences, front-loaded with purpose, then usage routing, then behavioral constraints. No filler or repetition; every clause carries information.

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?

For an 8-parameter write tool with full schema coverage, no output schema, and annotations present, the description covers purpose, usage routing, and side-effect profile. The only gap is duplicate/return behavior on a non-idempotent write, which is minor.

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

Parameters3/5

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

Schema description coverage is 100% with per-field descriptions and enum values, so the schema does the heavy lifting. The description's list of knowledge kinds loosely maps to the type enum but adds no format, syntax, or selection guidance beyond it. Baseline 3 is appropriate.

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?

Specific verb+resource (store durable project knowledge that survives across sessions) with the durable/temporary distinction made explicit against the checkpoint sibling. An agent can tell immediately what this tool produces and how it differs from checkpoint without opening either schema.

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?

States exactly when to use it (stable decisions, constraints, gotchas, failures, hypotheses, reusable knowledge) and names the alternative with its own condition (checkpoint for temporary task progress). Nothing is left to inference.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

resumeResume Saved TaskA

Resume and remove the top saved task from the task stack. Use after tasks shows resumable work; use tasks when you only need to inspect the stack. This changes local task state, is non-idempotent, does not delete durable memories, and performs no network access.

NameRequiredDescriptionDefault

No parameters

TDQS

A4.6/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=false and idempotentHint=false, but the description adds genuinely new context: it removes an entry from a stack (pop semantics), leaves durable memories intact, and performs no network access. The non-destructive clarification is valuable since 'remove' could otherwise read as data loss, though it restates non-idempotency already in 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?

Two sentences, both front-loaded: the operation first, then routing guidance and behavioral guarantees. No filler, and every clause carries information an agent needs.

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?

For a zero-param mutation with no output schema, the description covers the operation, the routing decision, state side effects, and network behavior. The only gap is what 'resume' returns (the resumed task content), which an agent might reasonably want to know, but annotations and description together are otherwise sufficient.

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 tool takes zero parameters, so there is nothing for the description to disambiguate; baseline 4 applies. No parameter-related claims are needed or missing.

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?

States a precise verb pair and resource: 'Resume and remove the top saved task from the task stack.' This distinguishes it from sibling 'tasks' (inspect) and from 'checkpoint'/'remember' by naming the exact stack operation performed.

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 names the precondition ('Use after tasks shows resumable work') and the alternative ('use tasks when you only need to inspect the stack'). Both when-to-use and when-not-to-use are covered with the sibling named.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

statusInspect Memory StatusA
Read-onlyIdempotent

Return memory, checkpoint, index, and recovery status for the current project. Use for diagnostics and health checks. This operation is read-only and idempotent, does not modify memory or task state, and performs no network access.

NameRequiredDescriptionDefault

No parameters

Output Schema

NameRequiredDescription
memoriesNoTotal number of durable memories in the current project.
checkpointsNoTotal number of checkpoints in the current project.
index_statusNoCurrent retrieval index health or synchronization status.
last_checkpointNoMost recent checkpoint recorded for the current project.

TDQS

A4/5.0
Behavior3/5

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

Annotations already declare readOnlyHint, idempotentHint, destructiveHint=false and openWorldHint=false, and the description largely restates these (read-only, idempotent, no state modification, no network access). It adds no information beyond the structured hints, but it also contradicts nothing, so a 3 is appropriate.

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?

Three short sentences, front-loaded with the core action and scope, followed by usage and safety context. No filler and nothing repeated needlessly.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The operation is parameterless, an output schema exists to describe the return shape, and annotations cover the safety profile; nothing an agent needs in order to call this correctly is missing.

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 tool takes zero parameters, so there is nothing for the description to disambiguate; per the rubric, a 0-parameter tool has a 4 baseline. The description correctly avoids inventing inputs.

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?

States a specific verb (Return) and enumerates the exact resources covered (memory, checkpoint, index, recovery status for the current project), which is well beyond a restatement of the name "status". It stops short of explicitly distinguishing itself from the operational siblings like resume or checkpoint, so it lands just under a 5.

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?

"Use for diagnostics and health checks" gives a clear context for invoking it, implicitly steering the agent away from action-oriented siblings such as remember or resume. There are no explicit when-not conditions or named alternatives, which keeps it from a 5.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

tasksList Pending TasksA
Read-onlyIdempotent

List the current task stack and resumable task state. Use before resume when you need to inspect pending work without changing the stack. This operation is read-only and idempotent, does not modify task state, and performs no network access.

NameRequiredDescriptionDefault

No parameters

Output Schema

NameRequiredDescription
tasksNoSaved or resumable tasks in the current task stack, in server-defined order.

TDQS

A4.1/5.0
Behavior3/5

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

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint=false, so the description's 'read-only and idempotent, does not modify task state' largely restates structured data rather than adding new behavior. 'Performs no network access' is a marginal addition but is already implied by openWorldHint=false.

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?

Front-loads the purpose in the first sentence and stays to three short sentences, though the safety restatement ('read-only and idempotent, does not modify task state') is somewhat redundant with annotations and could be trimmed.

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?

With an output schema present, the description need not explain return values, and it covers purpose, timing, and the safe-read nature adequately for a zero-parameter read tool.

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 tool takes zero parameters, so the baseline is 4; there is no parameter semantics to document, and the description correctly says nothing about inputs.

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?

States a specific verb+resource ('List the current task stack and resumable task state') that an agent can distinguish from sibling tools like resume or status without opening a schema.

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?

Gives clear timing guidance ('Use before resume when you need to inspect pending work') and implicitly contrasts with resume by noting it leaves the stack unchanged, but does not explicitly name alternatives or when-not-to-use conditions beyond that.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Tool Schema Changelog

Recent tool additions, removals, and schema changes observed during successful MCP inspections.

  1. 8 tool updatesv0.1.0
    • First observedcheckpoint
    • First observedcontext
    • First observedguard
    • First observedremember
    • First observedresume
    • First observedsearch
    • First observedstatus
    • First observedtasks

TDQS

A4.2/5.0

Scored across 8 tools

Disambiguation4/5

Descriptions carefully cross-reference each other (checkpoint vs remember, context vs search, resume vs tasks) so boundaries are mostly clear. Minor potential overlap between checkpoint/remember and guard/status, but the descriptions resolve these distinctions well.

Naming Consistency4/5

All names are single lowercase words with no camelCase/snake_case mixing, giving a uniform visual style. However, it mixes verbs (checkpoint, resume, search, guard, remember) with nouns (context, status, tasks), so the convention is not fully predictable.

Tool Count5/5

Eight tools is well-scoped for a lightweight memory server, with each tool mapping to a distinct operation (write memory, write checkpoint, search, inspect, resume, diagnose). No tool feels redundant or bolted on.

Completeness4/5

Covers the core lifecycle: durable memory (remember/search), task state (checkpoint/context/resume/tasks), and diagnostics (status/guard). The one gap is the absence of an update/delete/forget operation for durable memories, which descriptions explicitly note they never remove, limiting correction of stale knowledge.

Maintenance

ActivityStale
ResponsivenessNo issues

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