Skip to main content
Glama

Was ist das?

MCP Light Memory ist ein leichtgewichtiges, lokal-first, persistentes Speichersystem für Coding-Agenten und MCP-Clients (Warp, OpenCode, JetBrains AI Assistant / PyCharm, Claude Code, Cursor). Es fungiert als Checkpoint- + Abrufschicht – es speichert den minimalen dauerhaften Zustand, der nötig ist, um komplexe Arbeit über Sitzungen hinweg fortzusetzen, ohne die vollständige Konversation im Kontextfenster des Modells zu behalten.

Wenn Ihr Agent eine Aufgabe startet, ruft er context auf und erhält relevante frühere Entscheidungen, Stolperfallen, Einschränkungen und Hypothesen zurück – sortiert, dedupliziert und vertrauensbegrenzt. Wenn er fertig ist, speichert er den Arbeitszustand als Checkpoint. In der nächsten Sitzung, selbst nach einem Neustart, ist der Speicher da.

Related MCP server: mcp-chest-memory

Warum verwenden?

Problem

Wie MCP Light Memory es löst

Agenten vergessen alles zwischen Sitzungen

Markdown-Dateien bleiben auf der Festplatte; der Agent ruft sie über BM25 + optionale Embeddings ab

Vollständiger Sitzungsverlauf ist zu groß für den Kontext

Es werden nur relevante Erinnerungen abgerufen (Token-budgetiert, MMR-diversifiziert)

Cloud-Abhängigkeit / Datenschutzbedenken

100 % lokal, offline, keine Netzwerkaufrufe, kein Daemon

Schwere Einrichtung / Abhängigkeiten

Null erforderliche Laufzeitabhängigkeiten (reine Python-3.8+-Standardbibliothek); optional sentence-transformers für besseres semantisches Abrufen

Prompt-Injection über gespeicherten Speicher

Jede abgerufene Erinnerung ist explizit trust: untrusted-Evidenz mit einer Injection-Warn-Heuristik (ADR-015)

Multi-Projekt-Isolation

Router mit Registry-Allowlist, write:false-harter Grenze, Subprozess-Isolation pro Aufruf

MCP-Protokoll-Drift

Dual-Era-Unterstützung: modern 2026-07-28 + Legacy 2024-11-05…2025-11-25

So funktioniert es (Mechanismen)

  • Markdown ist die Quelle der Wahrheit. Jede Erinnerung ist eine .md-Datei mit YAML-Frontmatter (id, type, status, tags, sources, links, valid_from, valid_to, supersedes). Menschenlesbar, diffbar, dauerhaft.

  • SQLite ist ein neu aufbaubarer Cache. BM25/FTS5-Index + optionale Embedding-Vektoren + Nutzungsverfolgung. Löschen Sie ihn und alles wird aus Markdown neu aufgebaut.

  • Abruf: reines Python-BM25 + optionale dichte Embeddings → RRF-Fusion → MMR-Diversifizierung → Policy-Boosts (Typ/Status/zeitlich) → Token-Budget-Schnitt. Adaptiver Modus: zuerst sparse, dicht nur bei schwachen Ergebnissen.

  • Lebenszyklus: remember → update → supersede (verlinkt in beide Richtungen, löscht nie Verlauf) → forget (archiviert, löscht nie) → timeline (zeitliche Ansicht). search --at YYYY-MM-DD für historische Abfragen.

  • Vertrauensgrenze: abgerufener Inhalt ist in === BEGIN/END INTERNAL_RAG MEMORY === mit einem SECURITY NOTICE-Header eingebettet. Strukturiertes JSON/MCP trägt trust: untrusted + optional security_flags: ["instruction_like_content"].

  • Evidenz-Frische: jedes Ergebnis enthält evidence_state (present/missing/unverifiable) für lokale pfadartige Evidenz – zur Abrufzeit abgeleitet, nie persistiert.

  • Multi-Projekt-Router: ein MCP-stdio-Server vor vielen Projekten über eine JSON-Registry. write:false blockiert mutierende Tools, bevor ein Kindprozess gestartet wird. Subprozess-Isolation pro Aufruf (kein gemeinsamer Zustand).

Einrichtung

Voraussetzungen

  • Python 3.8+ (verwendet den py-Launcher, python oder python3 – der Installer erkennt automatisch den echten Interpreter und lehnt den WindowsApps-Stub ab)

  • Git (das Zielprojekt muss ein Git-Repository sein)

  • Optional: pip install sentence-transformers numpy für besseres semantisches Abrufen

Die aktuelle Version wird durch die VERSION-Datei definiert – prüfen Sie diese (oder führen Sie mlm.py --version aus), anstatt eine erwartete Nummer fest zu kodieren.

Schnellstart

Klonen Sie dieses Repository einmal und installieren Sie es dann in ein beliebiges Projekt:

# 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

Der Installer:

  • kopiert Skill-Dateien + erstellt INTERNAL_RAG/ + AGENTS.md

  • führt init + checkpoint + validate aus (damit guard sofort OK ist)

  • registriert den MCP-Server automatisch in der Client-Konfiguration, wenn dies sicher möglich ist (oder meldet MANUAL_REQUIRED / gibt JetBrains-Anweisungen aus)

  • schreibt den absoluten Pfad zum verifizierten Python-Interpreter (übersteht Windows-PATH-Probleme)

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

Installationsmatrix

Ein Installer, vier Clients, zwei Konfigurationsbereiche. Vollständige Anleitung: docs/INSTALLATION.md.

Client

Projektbereich

Globaler Bereich

Warp (Konfigurationsschreiben automatisch; Projektaktivierung kann Genehmigung erfordern)

install.py . --client warp

install.py . --client warp --global

OpenCode stable (V1) (automatisch für sichere JSON-Konfigurationsschreibvorgänge)

install.py . --client opencode

install.py . --client opencode --global

OpenCode 2 (V2, Beta) (automatisch für sichere JSON-Konfigurationsschreibvorgänge)

install.py . --client opencode2

install.py . --client opencode2 --global

JetBrains AI / PyCharm (manuell in der IDE-Oberfläche)

install.py . --client jetbrains

install.py . --client jetbrains --global

  • --global ändert den Bereich der CLIENT-KONFIGURATION (~/.warp/.mcp.json vs. {repo}/.warp/.mcp.json, ~/.config/opencode/opencode.json vs. Projekt-opencode.json). Der Server zeigt weiterhin auf das Zielprojekt, in das Sie installiert haben.

  • Benötigen Sie einen globalen MCP-Endpunkt für viele Repositories? Verwenden Sie den Multi-Projekt-Router – docs/MCP-MULTI-PROJECT.md.

  • JetBrains/PyCharm wird unterstützt, nicht vollautomatisch: Der Installer bereitet das JSON + das Arbeitsverzeichnis vor; Sie fügen den Server unter Settings → Tools → AI Assistant → MCP hinzu und wählen Server level = Project oder Global.

  • Manuelle Einrichtung (ohne Installer) pro Client: docs/INSTALLATION.md + Client-Seiten (Warp · OpenCode).

Zero-Shot: Copy-Paste-Prompts für Warp und OpenCode

Sie können einen dieser Prompts direkt in den Client-Agenten einfügen. Ersetzen Sie C:\Projects\App durch den tatsächlichen Ziel-Repository-Pfad.

Warp – Installation für ein Projekt:

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 – globale Client-Konfiguration für ein Projekt:

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 – Installation für ein Projekt (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 – globale Client-Konfiguration für ein Projekt (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.

Für OpenCode 2 / V2 verwenden Sie dieselben Prompts, geben aber explizit OpenCode 2 / V2 an und verlangen --client opencode2. Weitere Varianten: docs/ZERO-SHOT-SETUP-PROMPTS.md.


Konfigurationsdetails

Warp

Warp liest MCP-Serverkonfigurationen aus ~/.warp/.mcp.json (global, automatischer Start) oder {repo}/.warp/.mcp.json (Projekt, erfordert einen manuellen Schalter gemäß Warp-Dokumentation). Form: mcpServers.<name> mit command, args, working_directory (immer setzen – der Speicher wird daraus aufgelöst). Siehe examples/warp.example.json und docs/WARP-SETUP.md.

OpenCode stable (V1)

OpenCode liest opencode.json/.jsonc im Projektstamm oder ~/.config/opencode/opencode.json global. V1-Server sind flach unter mcp.<name> (kein servers-Unterschlüssel) mit enabled: true und command als Array – siehe examples/opencode-legacy.example.json und docs/OPENCODE.md.

OpenCode 2 (V2, Beta)

Dieselben Konfigurationsdateien, andere Form: mcp.servers.<name>, command als Array und kein enabled-Feld (V2 deaktiviert über disabled: true) – siehe examples/opencode-v2.example.jsonc und docs/OPENCODE.md.

JetBrains AI Assistant / PyCharm

PyCharm liest KEINE MCP-Konfigurationsdatei automatisch. Der Installer gibt einsatzbereites JSON + Arbeitsverzeichnis aus; Sie fügen den Server unter Settings → Tools → AI Assistant → MCP (STDIO) hinzu und wählen Server level = Project oder Global. Siehe examples/jetbrains.example.json.


Multi-Projekt-Router

Eine MCP-Verbindung vor vielen Projekten – Registry-Allowlist, write:false-harte Grenze, Subprozess-Isolation pro Aufruf.

Registry-Datei (projects.json)

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

Warp-Konfiguration für den Router

{
  "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"
    }
  }
}

Siehe docs/MCP-MULTI-PROJECT.md für Details.


Arbeitsablauf

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

Kernbefehle (CLI-Alias: mlm.py oder Legacy 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

Pfadzuordnung (Rebranding: internal-rag → MCP Light Memory)

Neuer Name

Legacy-Pfad (für Kompatibilität beibehalten)

MCP Light Memory (Produkt)

internal-rag (veralteter Produktname)

mlm / mlm.py (primäre CLI)

irag.py (Legacy-Alias, funktioniert weiterhin)

mcp-light-memory (MCP-Servername)

internal-rag (Legacy, funktioniert weiterhin in Konfigurationen)

mcp-light-memory-router (Routername)

internal-rag-router (Legacy)

INTERNAL_RAG/ (Speicherordner – unverändert)

—

.agents/skills/internal-rag/ (Skill-Verzeichnis – unverändert)

—

Der On-Disk-Ordner INTERNAL_RAG/ und das Skill-Verzeichnis .agents/skills/internal-rag/ werden absichtlich unter ihren Legacy-Namen für Null-Migrations-Rückwärtskompatibilität beibehalten. Siehe docs/MIGRATION-TO-MCP-LIGHT-MEMORY.md.

Dauerhafter Speicher (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

Typen: decision, knowledge, constraint, gotcha, failure, hypothesis, session.

Aufgabenstapel (Unterbrechungen)

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

Konfiguration (.irag.yml, optional)

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 zeigt die effektive Konfiguration. mlm.py config --init schreibt eine Vorlage.

Optionale Embeddings (besseres Abrufen)

pip install -r requirements-optional.txt

Wenn das Paket verfügbar ist und .irag.yml embeddings: auto (Standard) enthält, verwendet das Abrufen Embeddings mit Fallback auf BM25. Zur Laufzeit mit --embeddings on|off|auto überschreiben.

Zwei Abrufprofile (siehe docs/EMBEDDINGS.md):

  • english-fast (Standard, all-MiniLM-L6-v2)

  • multilingual (intfloat/multilingual-e5-small) – für Polnisch-Englisch-Projekte

Offline / Air-Gapped

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>

Siehe docs/OFFLINE.md für Details.

Datenschutz & Git

Der Standard-Installationsmodus ist nur lokal. Der Installer verwendet .git/info/exclude, nicht die .gitignore des Projekts, damit lokale Speicher- und Integrationsdateien nicht versehentlich committet werden.

Vor der Veröffentlichung eines Projekts:

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

Erwartet: RESULT: PASS

Vollständige Entfernung aus einem Projekt

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

Das Deinstallationsprogramm erstellt ein Backup außerhalb des Repositorys und entfernt dann INTERNAL_RAG und seine Integrationen. Verwenden Sie --keep-memory, um die Memory-Daten zu erhalten.

Dokumentation

Struktur in einem Zielprojekt

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)

Wahrheitsquelle

  1. aktuelle Benutzeranweisungen, 2. aktueller Code/Tests/Konfiguration, 3. Spezifikationen/ADRs, 4. verifiziertes Memory, 5. Sitzungsnotizen, 6. Hypothesen.

Memory kann veraltet sein. Code hat Vorrang.

Lizenz

MIT.


Änderungshistorie

1.8.0 — Manuelle Einrichtung für JetBrains

  • --client jetbrains schreibt keine Dummy-Konfigurationsdatei mehr (PyCharm ignoriert MCP-Konfigurationsdateien). Stattdessen werden JSON zum direkten Einfügen und IDE-Menüanweisungen ausgegeben.

  • --unregister --client jetbrains gibt einen Hinweis aus, die Entfernung in der IDE-Benutzeroberfläche vorzunehmen.

1.7.2 — JetBrains cwd + clientspezifische Meldungen

  • JetBrains: schreibt working_directory als Hinweis + gibt WARNING mit dem genauen Pfad aus, der in Settings → Tools → AI Assistant → MCP festzulegen ist.

  • Clientspezifische Neustartmeldungen (PyCharm neu starten / Warp neu starten / OpenCode neu starten).

  • Memory store: <path> wird in der Installationsausgabe ausgegeben.

1.7.1 — Windows-Python-Stub-Fix

  • detect_python() lehnt den 0-Byte-Stub von WindowsApps ab; bevorzugt py -0p; überprüft jeden Kandidaten mit --version.

  • Verifizierung nach der Registrierung: führt --version unmittelbar nach dem Schreiben der Konfiguration aus und meldet PASS/FAIL.

  • --unregister löscht leere Konfigurationsdateien + übergeordnete Verzeichnisse (behebt das tote .warp/.mcp.json-Skelett → GUARD STALE).

1.7.0 — Rebranding zu MCP Light Memory

  • Komplettes Rebranding von internal-rag zu MCP Light Memory (mcp-light-memory). Neuer CLI-Alias mlm (mlm.py). Logo-/Icon-Assets. Migrationsdokument. GitHub-Rebranding-Checkliste.

  • Abwärtskompatibel: irag.py, INTERNAL_RAG/, alte MCP-Servernamen bleiben als veraltete Aliase erhalten.

  • 18 Rebranding-Konsistenztests.

1.6.1 — Härtung nach v1.6

  • Mutations-/Lebenszyklus-Benchmark (11 Szenarien). Vertrauensgrenze (ADR-015): trust: untrusted + security_flags. Aktualität der Belege (ADR-016): evidence_state. Skalen-Benchmark (100/1k/10k). Router-Sicherheitsregressionen (+12 Tests). Dokumentations-Konsistenztest. 249 Tests bestehen.

1.6.0 — Retrieval-Qualität + MCP 2026-07-28

  • Memory-Qualitäts-Benchmark (37 Fälle). MCP 2026-07-28 Dual-Era (server/discover, _meta, structuredContent, outputSchema). Registry: striktes write. Quellen im Chunk-Präfix. Adaptives Retrieval. Link-bewusster Kontext. consolidate --prepare. Router-Latenz-Benchmark. ADR-010…016.

1.5.0 — Abstention-Gate + Multi-Projekt-Router

  • Relevanz-/Abstention-Gate (--meta). FTS5-Kandidaten-Vorfilter. Multi-Projekt-MCP-Router. MCP-Protokoll-Härtung (reines stdout, SDK-verifiziert). 168 Tests.

1.4.0 — Chunking + Deduplizierung + temporaler Lebenszyklus

  • Abschnittsbewusstes Chunking (Schema v3). SimHash-Deduplizierung. Mehrsprachiges PL/EN-Profil. Temporaler Lebenszyklus (valid_from/valid_to/supersedes/--at). consolidate --dry-run.

1.3.0 — Persistenter Embedding-Cache

  • Float32-BLOBs auf Chunk-Ebene in SQLite. Mehrere Modelle koexistieren. index --vacuum/--embed-missing.

1.0.2 — Token-Budget + Datenschutz

  • Durchsetzung des Token-Budgets. Erkennung veralteter Memory-Daten. Duplikaterkennung. Datenschutz-Scan beim Schreiben. Auto-Checkpoint-Timer. Offline-/Air-Gapped-Paket.

1.0.0 — Erstveröffentlichung

  • BM25 + MMR-Retrieval. Vollständige Memory-CRUD. Task-Stack. MCP-Server (JSON-RPC stdio). Git-Hooks. Diagnostik. Export/Import. Token-Budget.

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

Related MCP Connectors

Related MCP Servers

  • A
    license
    Not graded
    quality
    B
    maintenance
    Provides a persistent, local-first memory for coding agents over MCP, enabling automatic recall and recording of past work, failures, and decisions to reduce repetition and token usage.
    MIT
  • A
    license
    Not graded
    quality
    A
    maintenance
    Provides persistent memory for AI coding agents via MCP, enabling agents to store and semantically recall facts, events, and lessons across sessions, all running locally without cloud dependencies.
    Apache 2.0
  • A
    license
    Not graded
    quality
    B
    maintenance
    Enables AI coding agents to maintain a private, local-first persistent memory by automatically saving, searching, and retrieving structured project memories through MCP, with hybrid keyword and embedding search, feedback-driven ranking, and no cloud dependency.
    2
    MIT