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Mnemosyne

by M4F-S

Mnemosyne 🧠

CI PyPI version Python 3.9+ MCP License: Apache-2.0

Produktionsreife, lokal-first hierarchische Gedächtnis-Engine für autonome KI-Agenten.

Mnemosyne stattet KI-Agenten (Hermes, OpenClaw, OpenManus, Claude Desktop, Cursor, Windsurf, CrewAI, LangChain) mit permanentem, strukturiertem Langzeitgedächtnis aus. Es verbindet menschenlesbare Obsidian Markdown Vaults mit Hochgeschwindigkeits-PostgreSQL + pgvector (HNSW) oder SQLite ohne Konfiguration und ermöglicht hybride Reciprocal Rank Fusion (RRF)-Suche, Wikilink-Wissensgraphen, Ebbinghaus-Zeitabklingung, agentenübergreifendes Fleet-Sharing und asynchrone Google-Drive-Cloud-Synchronisierung.


📑 Inhaltsverzeichnis


Related MCP server: Synapto

⚡ Vollständige Feature-Matrix

Feature

Beschreibung

Nutzen

🤖 MCP-Nativ (v2024-11-05)

Standardisierter stdio-JSON-RPC-Protokollserver

Nahtlose Integration für Claude, Cursor, Windsurf, Hermes, OpenClaw

🔎 HNSW-Vektorsuche mit hoher Trefferquote

pgvector-HNSW-Indizierung mit vector_cosine_ops (m=16, ef_construction=64)

Sub-Millisekunden-Vektorabruf ohne Clustering-Neutraining

⚖️ Hybrider RRF-Abruf

Reciprocal Rank Fusion aus Dense Embeddings (1.0) + GIN FTS (0.8) + Graph (0.6) + Salience (0.2)

Erfasst exakte technische Schlüsselwörter (CVEs, Code-Tokens) & Fuzzy-Semantik

🏛️ Wing- & Room-Scoping

2-stufige Taxonomie (wing = Domäne/Projekt, room = Kanal/Thema)

Eliminiert Kontextfenster-Aufblähung & domänenübergreifende Halluzination

🌐 Agentenübergreifendes gemeinsames Gedächtnis

Zentrale shared_db, abfragbar über Multi-Agent-Fleets mit Credential-Screening

Kollektive Fleet-Intelligenz ohne Kompromittierung privater Datenbanken

☁️ Asynchrone Google-Drive-Synchronisierung

Lokal-first bidirektionale Synchronisierungs-Engine mit MD5-Diffing und .conflict.md-Branch-Auflösung

Sub-Millisekunden-Agent-I/O lokal + automatische Cloud-Backups & Team-Sharing

⏳ Ebbinghaus-Zeitabklingung

Exponentieller Abfall $Salience_t = Salience_0 \times (0.95)^{\Delta t}$ mit 90-Tage-Auto-Archivierung

Entfernt automatisch transientes Rauschen, während aktive Erinnerungen scharf bleiben

📌 Pinned-Memory-Immunität

Permanente Immunität gegen Abklingung durch pinned=True oder #pinned-Tags

Garantiert, dass grundlegende Anweisungen und Kernregeln nie verblassen

📖 Obsidian Zettelkasten

Schreibt menschenlesbare Markdown-Notizen mit YAML-Frontmatter & [[Wiki Links]]

Direkte visuelle Inspektion, Bearbeitung und Graph-Visualisierung in Obsidian

📜 Turn-bewusster Ingestor

1.500-Zeichen-Sliding-Window-Chunking mit 200-Zeichen-Überlappung entlang von Turn-Grenzen

Bewahrt die gesamte Konversationshistorie, ohne Codeblöcke zu brechen

🛡️ Prompt-Injection-Schutz

Neutralisiert Kontroll-Tokens (`<

im_start

>, [INST]`) in Prosa; escaped XML-Kontext-Tags

Verhindert Memory-Poisoning und Context-Hijacking-Angriffe

⚡ FastEmbed-ONNX-Unterstützung

Ultra-leichte ONNX-Runtime-Embedding-Engine (~30MB RAM)

90% Speicherreduktion gegenüber schwerem PyTorch sentence-transformers

🔄 SQLite-Fallback ohne Konfiguration

Automatischer Fallback auf lokales SQLite, wenn PostgreSQL offline ist

100% Feature-Parität für eigenständige Entwickler-Workstations


🏗️ Systemarchitektur

                                ┌──────────────────────────────────────────────┐
                                │          AI Agents & LLM Frameworks          │
                                │  Hermes • OpenClaw • Claude • Cursor • Manus │
                                └──────────────────────┬───────────────────────┘
                                                       │ JSON-RPC (stdio) / Python SDK
                                                       ▼
┌─────────────────────────────────────────────────────────────────────────────────────────────────────────────┐
│                                           Mnemosyne Core (v3.4.0)                                           │
│                                                                                                             │
│  ┌─────────────────────────┐  ┌─────────────────────────┐  ┌─────────────────────────────────────────────┐  │
│  │ Admission & Security    │  │ Multi-Backend Embedder  │  │ Turn-Aware Session Ingestor                 │  │
│  │ - Secret Regex Guard    │  │ - FastEmbed (ONNX 30MB) │  │ - Turn boundary splitting                   │  │
│  │ - Injection Neutralizer │  │ - Remote Microservice   │  │ - 1500-char linear sliding window           │  │
│  │ - Path Traversal Guard  │  │ - SentenceTransformers  │  │ - Sequential [[Wiki Link]] chaining         │  │
│  └─────────────────────────┘  └─────────────────────────┘  └─────────────────────────────────────────────┘  │
│                                                                                                             │
│  ┌───────────────────────────────────────────────────────────────────────────────────────────────────────┐  │
│  │                                 Hybrid RRF Retrieval & Graph Ranker                                   │  │
│  │            RRF Score = 1.0 * Dense(HNSW) + 0.8 * Keyword(FTS) + 0.6 * Graph + 0.2 * Salience          │  │
│  └───────────────────────────────────────────────────────────────────────────────────────────────────────┘  │
└───────────────────────────────────────┬───────────────────────────────────────┬─────────────────────────────┘
                                        │                                       │
                ┌───────────────────────┴───────────────────────┐               │
                ▼                                               ▼               ▼
 ┌─────────────────────────────┐                 ┌─────────────────────────────────────────────┐
 │    PostgreSQL + pgvector    │                 │       Obsidian Markdown Vault (Local)       │
 │ - HNSW vector_cosine_ops    │                 │ - Human-Readable Markdown + YAML Frontmatter │
 │ - GIN tsvector English FTS  │                 │ - [[Wikilink]] Knowledge Graph Visualizer   │
 │ - Self-Healing DB Pool      │                 │ - Atomic Writes with EXDEV Fallback         │
 │ - Private DB + shared_db    │                 └──────────────────────┬──────────────────────┘
 └─────────────────────────────┘                                        │ Async Background Sync
                                                                        ▼
                                                 ┌─────────────────────────────────────────────┐
                                                 │              Google Drive Cloud             │
                                                 │ - Service Account / OAuth2 Authentication    │
                                                 │ - MD5 Checksum Verification                 │
                                                 │ - Sibling .conflict-TIMESTAMP.md Resolution │
                                                 └─────────────────────────────────────────────┘

🧠 Deep Dive in die Kernfunktionen

1. Hierarchische Wing- & Room-Taxonomie

Gedächtnis-Kreuzkontamination ist ein häufiges Fehlermuster in Multi-Agent-Fleets. Mnemosyne strukturiert das Gedächtnis als 2-stufigen physischen Palast:

  • wing (Domäne/Projekt): Obergrenze der Domäne (z. B. ecommerce, pentest, devops, shared).

  • room (Thema/Kanal): Granulare Themenpartition (z. B. database, firewall, stripe_api).

Abfragen können eng auf einen bestimmten Wing oder Room eingegrenzt werden, sodass Marketing-Prompts keine Penetrationstest-Ergebnisse abrufen.

2. Hybride Reciprocal Rank Fusion (RRF)-Suche

Standard-Vektorsuche versagt bei exakten technischen Zeichenketten (z. B. CVE-2024-38077, 0x7fff5fbff8c0), während Stichwortsuche bei semantischen Konzepten versagt. Mnemosyne führt einen Multi-Kandidaten-Abruf durch und führt die Ergebnisse mittels gewichteter RRF zusammen:

$$\text{RRF Score}(d) = \sum_{m \in \text{modes}} w_m \cdot \frac{1}{k + \text{rank}_m(d)} + 0.2 \cdot \text{Salience}(d)$$

  • Dichte HNSW-Vektorsuche: Gewicht $1.0$ (Kosinus-Distanz über 384-dimensionale Embeddings).

  • PostgreSQL-Volltextsuche: Gewicht $0.8$ (tsvector gewichtet mit Titel als A und Inhalt als B).

  • Rekursive Graph-Traversierung: Gewicht $0.6$ (Rekursiver CTE, der 1-Hop- und 2-Hop-[[Wiki Links]] entdeckt).

  • Memory-Salience-Engine: Gewicht $0.2$ (Wichtigkeits-Score von $0.0$ bis $1.0$).

3. Agentenübergreifende gemeinsame Gedächtnisschicht (shared_db)

In autonomen Multi-Agent-Umgebungen halten Agenten isolierte private Datenbanken (toy_db, old_db, candy_db usw.) vor, um Zustandskorruption zu verhindern. Kollektive Intelligenz erfordert jedoch das Teilen globaler Richtlinien und verifizierter Fakten.

  • Veröffentlichung: Mit memory_publish_shared werden geprüfte Notizen in shared_db veröffentlicht.

  • Credential-Screening: Inhalte werden mit strengen Regex-Filtern auf Anthropic-Schlüssel (sk-ant-), Google-Gemini-Schlüssel (AIza...), HuggingFace-Tokens (hf_...), OpenAI-Schlüssel (sk-proj-...), AWS-Zugriffsschlüssel (AKIA...), Slack-Tokens (xox-) und private Schlüssel geprüft.

  • Fail-Soft-Abruf: Wenn ein Agent das Gedächtnis abfragt, fragt memory_recall sowohl den privaten Speicher als auch shared_db ab. Ist die gemeinsame Datenbank vorübergehend nicht erreichbar, degradiert das System elegant, ohne den Agenten zu unterbrechen.

4. Ebbinghaus-Zeitabklingung & Pinned-Immunität

Erinnerungen verlieren mit der Zeit natürlich an Relevanz. Mnemosyne implementiert Herman Ebbinghaus' exponentielle Vergessenskurve:

$$\text{Salience}(t) = \text{Salience}0 \times (0.95)^{\Delta t{\text{days}}}$$

  • Touch-Feedback: Der Zugriff auf eine Erinnerung aktualisiert last_accessed_at und setzt deren Abklingung zurück.

  • Nächtliche Auto-Archivierung: Die Konsolidierung überführt automatisch Notizen mit $\text{Salience} < 0.05$ und ohne Zugriff seit $>90\text{ Tagen}$ in status = 'archived'.

  • Pinned-Immunität: Systemregeln, Kernrichtlinien oder Notizen, die mit pinned=True markiert oder mit #pinned getaggt sind, erhalten permanente Immunität gegen Zeitabklingung ($\text{Salience} = 1.0$).

5. Obsidian Markdown Vault & bidirektionaler Graph

Jede Erinnerung, die ein Agent erstellt, wird gleichzeitig als menschenlesbare .md-Datei in Ihrem Obsidian-Vault geschrieben:

  • Zettelkasten-Frontmatter: Enthält title, date, tags, type, salience, wing und room.

  • Wissensgraph: Zielnotizen, die als [[Zielnotiz]] erwähnt werden, werden automatisch in bidirektionale Kanten in PostgreSQL geparst.

  • Live-Inspektion: Öffnen Sie Obsidian auf Ihrem Desktop oder Mobilgerät und erkunden Sie das kollektive Gedächtnis Ihres Agent-Fleets in Obsidians interaktiver Graph-Ansicht.

6. Turn-bewusster Verbatim-Sitzungs-Ingestor

Konversationstranskripte enthalten oft entscheidende Nuancen, die bei verlustbehafteter Zusammenfassung verloren gehen. memory_ingest_session:

  • Teilt rohe Transkripte entlang von Turn-Grenzen (User:, Assistant:, ### Turn, **Human**:).

  • Wendet für Turns über 1.500 Zeichen ein lineares Sliding Window an (1.500 Zeichen mit 200 Zeichen Überlappung).

  • Verkettet sequenzielle Chunks mit [[Session ... Turn 01 Part 02]]-Wikilinks und bewahrt so Codeblöcke, Ausführungsabläufe und den Konversationsfluss.

7. Asynchrone Google-Drive-Cloud-Synchronisierung

Halten Sie Ihre Agent-Vaults sicher gesichert und über mehrere Maschinen oder Mobilgeräte hinweg synchronisiert:

  • Lokale Geschwindigkeit: Agent-Tool-Aufrufe werden mit lokaler SSD-Geschwindigkeit (<1ms) ausgeführt, ohne auf die Netzwerklatenz von Google Drive zu warten.

  • Hintergrund-Daemon / Cron-Synchronisierung: Scannt Vault-Dateien, berechnet MD5-Prüfsummen und synchronisiert Deltas bidirektional mit Google Drive.

  • Konfliktlösung: Wenn eine Datei gleichzeitig auf Google Drive und im lokalen Agent-Vault geändert wird, speichert Mnemosyne die eingehende Version als NoteName.conflict-YYYYMMDD-HHMMSS.md, um Datenverlust zu verhindern.

  • Authentifizierung: Unterstützt Google Cloud Service Account JSON (GOOGLE_APPLICATION_CREDENTIALS, GDRIVE_SERVICE_ACCOUNT_JSON) und OAuth2-Benutzertokens (GDRIVE_TOKEN_JSON).

8. Flexible Embedding-Backends (FastEmbed / Microservice / Lokal)

Mnemosyne passt sich jedem Bereitstellungs-Ressourcenbudget an:

  1. FastEmbed ONNX Runtime (Empfohlen für eigenständige Knoten): Verwendet ONNX Runtime C++-Ausführung (~30MB RAM). Kein PyTorch-Overhead.

  2. Zentralisierter Microservice (mnemosyne.embed_service): Hostet sentence-transformers in einem einzigen dedizierten Container, der mehrere Agent-Container über HTTP bedient (MEMORY_EMBED_URL).

  3. Lokale SentenceTransformers: Eigenständige PyTorch-Ausführung (all-MiniLM-L6-v2, 384-dimensional).

  4. Deterministischer Hash-Fallback: Mathematischer Vektor-Hash mit null RAM für extrem ressourcenbeschränkte Umgebungen.

9. Defense-in-Depth-Sicherheit & Injektionsschutz

  • Pfad-Traversal-Immunität: Doppelt aufgelöste kanonische Pfadprüfungen (is_relative_to) stellen sicher, dass Dateioperationen die Vault-Root nicht verlassen können.

  • Atomare Sibling-Schreibvorgänge: Dateien werden in temporäre Sibling-Dateien (.note.pid.tmp) geschrieben und atomar umbenannt, mit automatischem Fallback für EXDEV-geräteübergreifende Volume-Mounts.

  • Neutralisierung von Steuerungstokens: Neutralisiert LLM-Injektionstokens (<|im_start|>, <|system|>, [INST], <<SYS>>) in Prosa, während Codeblöcke unverändert erhalten bleiben.

  • Strukturierte XML-Kontextumhüllung: Abgerufene Erinnerungen werden in <recalled_memory_context id="..." title="..." source="...">-Tags mit interner Tag-Escaping eingebettet, sodass Host-LLMs abgerufene Erinnerungen niemals mit aktiven Systemanweisungen verwechseln.


🛠️ MCP-Tool-Referenz (8 Tools)

Alle 8 Tools werden Agenten nativ über standardmäßiges MCP JSON-RPC stdio bereitgestellt:

1. memory_remember

Speichert eine private Erinnerungsnotiz im Vault mit semantischem Embedding, Tags und hierarchischer Bereichsdefinition.

{
  "title": "PostgreSQL HNSW Tuning",
  "content": "For datasets >10,000 vectors, use HNSW with m=16 and ef_construction=64 for optimal recall.",
  "tags": ["database", "pgvector", "performance"],
  "wing": "engineering",
  "room": "databases",
  "salience": 0.8,
  "pinned": true
}

2. memory_publish_shared

Veröffentlicht einen bereinigten, geprüften Befund oder eine Richtlinie im gemeinsamen agentenübergreifenden Flotten-Speicher (shared_db).

{
  "title": "Fleet Security Policy: SSL Verification",
  "content": "All internal agent HTTP requests must enforce SSL certificate validation.",
  "tags": ["security", "policy"],
  "wing": "shared",
  "room": "general"
}

3. memory_recall

Durchsucht Erinnerungen in privaten und gemeinsamen Flotten-Datenbanken mithilfe von hybridem RRF, HNSW-Vektoren, Schlüsselwörtern oder Graphen.

{
  "query": "HNSW index configuration parameters",
  "mode": "hybrid",
  "top_k": 5,
  "scope": {
    "wing": "engineering",
    "room": "databases"
  },
  "include_shared": true
}

4. memory_ingest_session

Erfasst und chunked ein vollständiges Gesprächstranskript wörtlich entlang der Wendepunkt-Grenzen.

{
  "transcript": "User: How do we configure pgvector?\nAssistant: Use CREATE EXTENSION vector; then create an HNSW index.",
  "wing": "engineering",
  "room": "sessions"
}

5. memory_timeline

Untersucht kürzliche Speicheroperationen (remember, recall, remind, consolidate) in chronologischer Reihenfolge.

{
  "limit": 20
}

6. memory_history

Zeigt Versionsverlauf und vergangene Bearbeitungsschnappschüsse einer bestimmten Erinnerungsnotiz vor Aktualisierungen an.

{
  "title": "PostgreSQL HNSW Tuning",
  "limit": 5
}

7. memory_remind_me

Plant eine zukünftige prospektive Erinnerung oder wiederkehrende Aufgabe.

{
  "title": "Rotate Database Credentials",
  "content": "Verify that all 5 agent connection pools are refreshed with new passwords.",
  "trigger_at": "2026-09-01T00:00:00Z",
  "recurring": "monthly"
}

8. memory_audit

Liefert Echtzeit-Metriken zur Speichergesundheit, Store-Backend-Status, Anforderungszahlen und aktive Wings.

{}

🌐 Produktionsarchitektur für Multi-Agenten-Flotten

In Multi-Agenten-Produktionsumgebungen (wie der 5-Agenten-Hermes-Flotte) isoliert Mnemosyne Agent-Datenbanken in einem internen Docker-Netzwerk und bietet gleichzeitig gemeinsame Intelligenz:

                                  ┌─────────────────────────────────────────┐
                                  │      Production VPS (${VPS_HOST})      │
                                  └────────────────────┬────────────────────┘
                                                       │
         ┌───────────────────┬──────────────────┼───────────────────┬──────────────────┐
         ▼                   ▼                  ▼                   ▼                  ▼
┌──────────────────┐┌──────────────────┐┌──────────────────┐┌──────────────────┐┌──────────────────┐
│   hermes-agent   ││ hermes-assistant ││ hermes-marketing ││  hermes-pentest  ││  hermes-trader   │
│      (Toy)       ││      (Old)       ││     (Candy)      ││     (Pencil)     ││      (Coin)      │
│   Database:      ││   Database:      ││   Database:      ││   Database:      ││   Database:      │
│     toy_db       ││     old_db       ││     candy_db     ││     pencil_db    ││     trader_db    │
└────────┬─────────┘└────────┬─────────┘└────────┬─────────┘└────────┬─────────┘└────────┬─────────┘
         │                   │                  │                   │                  │
         └───────────────────┴──────────────────┼───────────────────┴──────────────────┘
                                                │
                                                ▼
                               ┌───────────────────────────────────┐
                               │   PostgreSQL + pgvector (HNSW)    │
                               │   - Private DBs: toy_db, old_db.. │
                               │   - Shared DB:   shared_db        │
                               └───────────────────────────────────┘

🚀 Schnellstart & Installation

1. Installation

# Standard installation
pip install mnemosyne-memory

# With Google Drive Cloud Synchronization support
pip install "mnemosyne-memory[gdrive]"

# With lightweight FastEmbed ONNX support (~30MB RAM)
pip install "mnemosyne-memory[fastembed]"

# Full installation (All extras + Dev dependencies)
pip install "mnemosyne-memory[dev,gdrive,fastembed,embed-service]"

2. Ausführen mit Docker Compose

Starten Sie den PostgreSQL- + pgvector-Container:

docker compose up -d

3. MCP-Server starten

# Standalone with local SQLite (Zero configuration)
python -m mnemosyne server

# With PostgreSQL + pgvector
MEMORY_DB_DSN="postgresql://mnemosyne:***@localhost:5432/my_agent_db" python -m mnemosyne server

🤖 Integrationsrezepte für Agent-Frameworks

1. Claude Desktop (claude_desktop_config.json)

{
  "mcpServers": {
    "mnemosyne": {
      "command": "python3",
      "args": ["-m", "mnemosyne", "server"],
      "env": {
        "MEMORY_VAULT_PATH": "/Users/username/Documents/Obsidian/AgentVault",
        "MEMORY_DEFAULT_WING": "claude"
      }
    }
  }
}

2. Cursor IDE (.cursor/mcp.json)

{
  "mcpServers": {
    "mnemosyne": {
      "command": "python3",
      "args": ["-m", "mnemosyne", "server"],
      "env": {
        "MEMORY_VAULT_PATH": "./.vault",
        "MEMORY_DEFAULT_WING": "codebase"
      }
    }
  }
}

3. Hermes-Agent (~/.hermes/config.yaml)

mcp_servers:
  obsidian_memory:
    command: python3
    args: ["-m", "mnemosyne", "server"]
    env:
      MEMORY_DB_DSN: "postgresql://mnemosyne:***@${DB_HOST}:5432/toy_db"
      MEMORY_SHARED_DSN: "postgresql://mnemosyne:***@${DB_HOST}:5432/shared_db"
      MEMORY_VAULT_PATH: "/opt/data/vault"

4. OpenClaw (openclaw-config.yaml)

plugins:
  mcp_servers:
    mnemosyne:
      command: "python3"
      args: ["-m", "mnemosyne", "server"]
      env:
        MEMORY_VAULT_PATH: "~/.openclaw/vault"
        MEMORY_DEFAULT_WING: "openclaw"

5. Python-SDK & autonome Agent-Skripte

from mnemosyne import UnifiedMemorySystem

mem = UnifiedMemorySystem(
    vault_path="~/.agent/vault",
    dsn="postgresql://mnemosyne:***@localhost:5432/agent_db",
    shared_dsn="postgresql://mnemosyne:***@localhost:5432/shared_db"
)

# Remember fact
mem.remember(
    title="Kubernetes Cluster Policy",
    content="Deployments in staging must specify resource memory limits.",
    wing="infrastructure",
    room="k8s",
    tags=["kubernetes", "policy"],
    pinned=True
)

# Hybrid recall
results = mem.recall(
    query="staging memory limits",
    mode="hybrid",
    scope={"wing": "infrastructure"}
)

for r in results:
    print(r["title"], "->", r["formatted_context"])

💻 Vollständige CLI-Befehlsreferenz

Mnemosyne enthält eine voll ausgestattete Verwaltungs-CLI:

# 1. Store a memory note
python -m mnemosyne remember "API Architecture" "Uses Bearer JWT auth." --tags security auth --wing backend --room api --salience 0.8 --pinned

# 2. Publish shared fleet memory
python -m mnemosyne publish-shared "Global Production Policy" "Always check SSL certs." --wing devops

# 3. Search memories
python -m mnemosyne recall "JWT authentication" --mode hybrid --top-k 5 --wing backend

# 4. View activity timeline
python -m mnemosyne timeline --limit 20

# 5. Trigger Ebbinghaus decay & link reconciliation
python -m mnemosyne consolidate --decay-rate 0.95 --archive-threshold 0.05

# 6. Check system statistics & health
python -m mnemosyne stats

# 7. Synchronize with Google Drive (One-off pass)
python -m mnemosyne sync-gdrive --folder "My-Agent-Vault" --credentials service-account.json

# 8. Run Google Drive Sync as a background daemon
python -m mnemosyne sync-gdrive --daemon --interval 60

# 9. Run standalone Centralized Embedding Microservice
python -m mnemosyne embed-service --host 0.0.0.0 --port 8000 --model all-MiniLM-L6-v2

⚙️ Referenz der Umgebungsvariablen

Variable

Standard

Beschreibung

MEMORY_VAULT_PATH

~/.mnemosyne/vault

Dateisystempfad zum lokalen Obsidian-Markdown-Vault-Verzeichnis

MEMORY_DB_DSN

(keine)

PostgreSQL-DSN (z. B. postgresql://user:pass@host:5432/db). Wenn nicht gesetzt, wird SQLite verwendet

MEMORY_SHARED_DSN

(keine)

PostgreSQL-DSN für die optionale agentenübergreifende gemeinsame Flotten-Datenbank

MEMORY_AGENT_NAME

local-agent

Kennung für den Ursprungsagenten in Multi-Agenten-Flottenbereitstellungen

MEMORY_EMBED_URL

(keine)

URL des entfernten zentralisierten Embedding-Microservice (z. B. http://localhost:8000)

MEMORY_REQUIRE_POSTGRES

false

Setzen Sie true, um einen Fehler auszulösen, anstatt auf SQLite zurückzufallen, wenn PostgreSQL fehlschlägt

GOOGLE_APPLICATION_CREDENTIALS

(keine)

Dateipfad zur Google Cloud Service Account JSON für die Google-Drive-Synchronisierung

GDRIVE_SERVICE_ACCOUNT_JSON

(keine)

Stringifizierter JSON-Inhalt der Google Cloud Service Account-Anmeldedaten

GDRIVE_TOKEN_JSON

(keine)

Stringifizierter JSON-Inhalt des autorisierten Google-OAuth2-Benutzertokens

TOKENIZERS_PARALLELISM

false

Deaktiviert HuggingFace-Tokenizer-Forks, um die Integrität des stdio-JSON-RPC-Streams zu bewahren

HF_HUB_DISABLE_PROGRESS_BARS

1

Deaktiviert Fortschrittsbalken in stdio, um MCP-Streams makellos zu halten

HF_HUB_OFFLINE

0

Setzen Sie 1, um SentenceTransformers zu 100 % offline mit lokalem Cache auszuführen

TRANSFORMERS_OFFLINE

0

Setzen Sie 1, um zu verhindern, dass transformers externe HuggingFace-Netzwerkanfragen stellt


🧪 Tests & Benchmarks

Mnemosyne pflegt eine umfassende Testsuite (Unit-Tests, Sicherheits-Injektionstests, SQLite-Tests und Live-PostgreSQL-pgvector-Integrationstests):

# Run all unit tests
pytest tests/ -v -m "not integration"

# Run full test suite including live PostgreSQL + pgvector tests
MEMORY_DB_DSN="postgresql://mnemosyne:***@localhost:5432/test_db" pytest tests/ -v

📄 Lizenz

Apache-2.0-Lizenz. Entwickelt für das offene Ökosystem autonomer Agenten. Siehe LICENSE für vollständige Details.

Available Tools

9 tools
memory_assemble_contextA

Retrieve and format high-salience memories into a strict token-budgeted XML prompt block ready for direct agent context injection.

NameRequiredDescriptionDefault
modeNohybrid
queryYesSearch query to retrieve context for
scopeNoOptional wing/room filter scope
max_tokensNoMaximum token budget for the assembled context
include_sharedNoWhether to include shared fleet knowledge

TDQS

A3.7/5.0
Behavior4/5

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

No annotations are provided, so the description carries the full behavioral burden. It discloses that the tool retrieves rather than mutates, filters for 'high-salience', enforces a 'strict token-budget', and formats into XML. This is useful, though it doesn't explain how salience is determined or how failures/token-truncation are handled.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single dense, front-loaded sentence with no filler. It names the primary action, the input type, the processing behavior, and the output format in an efficient structure.

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

Completeness3/5

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

The description covers the key output (XML prompt block) but leaves important gaps for a tool with five parameters and no annotations: the meaning of each `mode`, how `scope` is applied, the effect of `include_shared`, and expected behavior when the token budget is exceeded. The schema fills some gaps, but the description alone isn't fully complete for correct invocation.

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 80%, so the schema already documents most parameter meanings. The description adds some context around token budgeting and salience, but it doesn't elaborate on the `mode` enum, `scope` object, or `include_shared` flag beyond what the schema fields already say. This is adequate but not additive.

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

Purpose4/5

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

The description clearly states the action ('Retrieve and format') and resource ('high-salience memories'), and specifies the unique output format: 'strict token-budgeted XML prompt block ready for direct agent context injection.' It doesn't explicitly name sibling alternatives, but the XML formatting and context-injection purpose distinguish it from tools like memory_recall or memory_timeline.

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

Usage Guidelines3/5

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

The intended use is implied: assemble memory context for direct agent injection. It does not name when to use this over alternatives, nor provide any when-not-to-use guidance. The phrase 'ready for direct agent context injection' gives some context, but an agent would need to infer the comparison with memory_recall.

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

memory_auditA

Get memory system statistics, health check, active wings, and version info

NameRequiredDescriptionDefault

No parameters

TDQS

A3.5/5.0
Behavior3/5

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

No annotations are provided, so the description carries the burden of behavioral disclosure. The verb 'Get' strongly implies a read-only operation, and the listed outputs suggest no mutation. However, it does not explicitly state that no state changes occur, whether permissions are required, or whether the health check performs any active diagnostics.

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?

A single, compact sentence with no filler. The verb is front-loaded and each listed item contributes meaning. Every word earns its place, and the structure is easy for an agent to parse quickly.

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 zero parameters and no output schema, the description needs to convey what the tool returns. It does so by listing four output categories: statistics, health check, active wings, and version info. This is adequate for simple invocation, though the term 'active wings' is domain-specific and not expanded.

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 has zero parameters, so the schema provides no parameter documentation. Per the baseline for a zero-parameter tool, the description does not need to compensate for parameter gaps; it simply describes the operation's outputs, which is sufficient.

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

Purpose4/5

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

The description opens with the specific verb 'Get' and names distinct resources: memory system statistics, health check, active wings, and version info. This makes the tool's focus clear and differentiates it from content-oriented siblings like memory_recall or memory_history, though it does not explicitly contrast itself with them.

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

Usage Guidelines2/5

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

There is no guidance on when to choose memory_audit over sibling tools. While the system-level subject matter implies a diagnostic use case, the description does not explicitly state when to run an audit versus when to use memory_history, memory_timeline, or memory_assemble_context.

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

memory_historyB

View version history of a specific memory note (previous edits).

NameRequiredDescriptionDefault
limitNo
titleYesThe title of the note to check history for

TDQS

B3.2/5.0
Behavior2/5

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

There are no annotations, so the description carries the full burden of behavioral disclosure. It only indicates a read-style operation ('View') and says nothing about the shape of the returned history, ordering, pagination, or how 'previous edits' are represented.

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?

The description is a single, front-loaded sentence with no filler. It is easy to parse and economical, though it may be too terse to convey all necessary behavioral and parameter guidance.

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

Completeness3/5

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

For a simple read tool, the description names the resource and indicates the action, and the required title parameter is sufficient to invoke it. However, with no output schema and no annotations, the lack of return-format and limit semantics leaves clear gaps for an agent trying to use the results correctly.

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

Parameters2/5

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

The title parameter is already documented in the schema, but the limit parameter has no description and the tool description adds no meaning for it. With 50% schema coverage, the description needed to compensate for the undocumented parameter and did not.

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

Purpose4/5

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

The description states a specific verb ('View') and a precise resource ('version history of a specific memory note'), with the clarifying parenthetical 'previous edits'. It is immediately clear what the tool does, but it does not explicitly contrast with sibling tools such as memory_timeline or memory_audit.

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?

The description conveys a clear use case: checking the previous edits of one specific note. It gives enough context for when an agent should invoke it, though it provides no explicit exclusions or alternative routing to sibling tools.

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

memory_ingest_sessionA

Ingest a full conversation transcript verbatim into memory, split along conversational turn boundaries.

NameRequiredDescriptionDefault
roomNosessions
wingNogeneral
transcriptYesThe full conversation text to ingest

TDQS

A3.7/5.0
Behavior3/5

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

With no annotations provided, the description carries the full behavioral disclosure burden. It usefully disloses that content is stored verbatim and split by turn boundaries, but it does not explain side effects, storage semantics, deduplication, limits, or whether repeated ingestion creates duplicates. These would be valuable for a memory-mutating tool.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

One sentence, no filler, and the most decision-relevant information is front-loaded. The phrase about splitting along turn boundaries is efficiently included without bloating the description.

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

Completeness3/5

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

For a simple ingestion tool, the core invocation path is clear: provide the full transcript. However, no output schema or annotations exist, and the optional room/wing parameters are unexplained, so an agent would need to infer their purpose from names or defaults. This leaves some contextual ambiguity around storage targeting.

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 coverage is only 33%, with only 'transcript' having a schema description. The description adds meaning to the requied parameter by specifying 'verbatim' and 'split along conversational turn boundaries', but it does not explain the optional 'room' and 'wing' parameters, which remain defined only by their names and defaults.

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

Purpose5/5

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

The description clearly identifies the action ('Ingest'), the resource ('full conversation transcript'), the destination ('into memory'), and the precise behavior ('verbatim, split along conversational turn boundaries'). This distinguishes it from sibling tools like memory_remember or memory_recall, which likely handle individual memories rather than bulk ingestion.

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

Usage Guidelines3/5

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

The description implies this is for full multi-turn conversation transcripts, which suggests a bulk ingest use case distinct from memory_remember or memory_recall. However, it does not explicitly state when not to use it or name alternative tools, leaving any routing decision to inference.

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

memory_publish_sharedA

Publish a sanitized, curated finding or policy to the cross-agent shared fleet memory.

NameRequiredDescriptionDefault
roomNogeneral
tagsNoTags
wingNoshared
titleYesTitle for the shared policy or finding
contentYesThe shared knowledge content in markdown (must not contain private credentials)

TDQS

A3.5/5.0
Behavior3/5

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

With no annotations, the description carries the behavioral burden. It discloses that this is a write/publish action with cross-agent visibility and imposes a sanitization constraint, which is valuable. However, it does not mention persistence, overwrite/append behavior, permissions, or what the call returns.

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?

A single front-loaded sentence with no filler. The action, object, and destination are stated directly, and every word adds meaning.

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

Completeness2/5

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

For a writing tool with five parameters, no annotations, and no output schema, this description is too thin. It omits expected call semantics such as return value, success/failure behavior, and the meaning of room/wing, leaving an agent to guess at important usage details.

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

Parameters2/5

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

The schema already documents title, content, and tags, but room and wing are undocumented and the description does not clarify them. The general phrase 'finding or policy' adds only minimal semantics to the content parameter and says nothing about how the publish target or routing fields behave.

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

Purpose5/5

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

The description names a specific verb ('Publish'), a concrete object ('a sanitized, curated finding or policy'), and a precise destination ('cross-agent shared fleet memory'). This clearly differentiates it from local memory tools in the sibling set such as memory_remember or memory_recall.

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

Usage Guidelines3/5

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

It implies the occasion ('sanitized, curated' material meant for shared use) but never explicitly says when to prefer this over memory_remember or memory_ingest_session, nor does it provide when-not-to-use exclusions. The shared-fleet scope is useful context, but the routing guidance 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.

memory_recallB

Search memory by semantic meaning, keywords, or graph. Retrieves from both private and shared stores.

NameRequiredDescriptionDefault
modeNo
queryYes
scopeNoRestrict search to a wing and/or room
top_kNo
include_sharedNoWhether to include cross-agent shared fleet memory

TDQS

B3.2/5.0
Behavior3/5

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

With no annotations, the description carries the behavioral burden. It discloses that the tool retrieves from both private and shared stores and that search can be semantic, keyword-based, or graph-based. However, it does not mention return format, whether any filtering or ranking applies, or any access caveats.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single concise sentence with no filler. The primary action and retrieval scope are front-loaded, making it easy to parse quickly.

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

Completeness2/5

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

Given 5 parameters, a nested scope object, no output schema, no annotations, and several related sibling tools, this description is too sparse. It leaves important invocation details such as mode defaults, query semantics, shared-memory handling, and result expectations unexplained.

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

Parameters2/5

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

Schema description coverage is only 40%, and the description compensates poorly. It loosely maps to the mode enum with 'semantic meaning, keywords, or graph' and mentions shared/private stores, but it does not clarify the required query parameter, the meaning of hybrid mode, top_k behavior, or the nested scope object.

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

Purpose4/5

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

The description uses a specific verb and resource: 'Search memory' and lists search methods (semantic, keyword, graph). It is clear about what the tool does, though it does not explicitly differentiate itself from sibling tools like memory_timeline or memory_history.

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

Usage Guidelines3/5

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

The description implies the tool is for searching/recalling stored memories, but it gives no explicit guidance on when to prefer memory_recall over alternatives like memory_assemble_context, memory_timeline, or memory_remind_me. Usage context is inferred rather than stated.

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

memory_rememberA

Store a private memory note in the vault with semantic embedding, tags, and hierarchical wing/room scope.

NameRequiredDescriptionDefault
roomNoTopic within the wing (e.g. woocommerce, firewall, docker)general
tagsNoCategorical tags
wingNoProject/domain grouping (e.g. ecommerce, security, devops)general
titleYesShort, unique title for the memory
pinnedNoSet true to make permanently immune to Ebbinghaus temporal decay
contentYesThe memory content in markdown
salienceNoImportance score 0.0-1.0 (default 0.5)

TDQS

A3.7/5.0
Behavior3/5

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

With no annotations, the description carries the full burden and it does add useful behavioral context: 'private,' 'vault,' 'semantic embedding,' and hierarchy. It does not disclose behaviors such as duplicate-title handling, temporal decay, or return value, which leaves some opacity for a state-changing tool.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

One sentence, front-loaded with the verb, and every detail—privacy, vault, embedding, tags, scope—earns its place. There is no filler or redundancy.

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

Completeness3/5

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

Schema plus description covers what to pass, but the absence of annotations and output schema, combined with seven sibling tools, leaves gaps: no routing guidance versus ingestion/sharing tools and no statement of what happens on success or when a title already exists. Complete enough for basic invocation but not fully self-sufficient.

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% and each parameter already has meaningful descriptions (e.g., room is 'Topic within the wing'; pinned explains Ebbinghaus decay). The description adds only the notion of hierarchy, not substantive parameter semantics, so the 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?

The description states a specific action ('Store') and a specific resource ('private memory note in the vault') while naming key processing details: semantic embedding, tags, and hierarchical wing/room scope. 'Private' distinguishes it from shared publication, and the write verb separates it from retrieval siblings like memory_recall.

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

Usage Guidelines3/5

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

Usage is implied by the store verb: use this when saving a private memory note. However, the description provides no explicit when-to-use or when-not-to-use guidance and names no alternatives, so an agent must infer the boundary against siblings like memory_ingest_session and memory_publish_shared.

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

memory_remind_meB

Schedule a future reminder or recurring task

NameRequiredDescriptionDefault
titleYes
contentNo
recurringNo
trigger_atYes

TDQS

B3.2/5.0
Behavior2/5

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

Because no annotations are provided, the description carries the full burden of behavioral disclosure. It only states the basic scheduling action and omits important traits such as trigger_at format, how recurrence is applied, side effects when the reminder fires, and whether a memory entry is also created.

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?

The description is a single efficient, front-loaded sentence with no wasted words. However, its brevity comes at the cost of necessary parameter and behavior details, so it is concise but not fully appropriately sized.

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

Completeness2/5

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

For a tool with four parameters, no annotations, schema description coverage of 0%, and no output schema, this description is too thin. An agent cannot confidently determine how to provide trigger_at or how recurring should behave, so the definition is materially incomplete.

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

Parameters2/5

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

Schema description coverage is 0%, so the description must compensate, but it names no parameters directly. It only hints at 'future' and 'recurring', leaving title, content, trigger_at format, and the relationship between recurring and trigger_at undocumented.

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

Purpose5/5

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

The description uses a specific verb ('Schedule') and identifies the resource ('future reminder or recurring task'). This clearly distinguishes it from retrospective siblings like memory_recall, memory_timeline, and memory_history, and from memory_remember which implies storage rather than scheduling.

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

Usage Guidelines3/5

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

The phrase 'Schedule a future reminder or recurring task' implies the core use case, but no explicit guidance is given about when to choose this over alternatives. There are no prerequisites, no exclusions, and no mention of sibling tools that might be more appropriate.

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

memory_timelineB

View recent memory operations (remember, recall, remind, consolidate) as a chronological timeline.

NameRequiredDescriptionDefault
limitNo

TDQS

B3.1/5.0
Behavior3/5

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

With no annotations, the description bears the burden of behavioral disclosure. 'View' and 'chronological timeline' imply a read-only listing, which is helpful, but no details are given about ordering direction, result format, pagination, or whether any side effects occur.

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?

A single, well-structured sentence that front-loads the main action and resource. Every word contributes value, and it includes useful examples of the operation types shown.

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

Completeness3/5

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

For a simple read-only timeline with one optional parameter, the description is mostly adequate. However, the lack of sibling differentiation, no mention of the `limit` parameter, and no output format details leave clear gaps for an agent deciding how to invoke it.

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

Parameters2/5

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

The single parameter `limit` has zero schema description coverage and the tool description does not mention it at all. While the parameter is somewhat self-explanatory from its name and default value, the description adds no meaning beyond the schema.

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

Purpose4/5

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

The description states a specific verb ('View') and a clear resource ('recent memory operations') with examples of operation types. It is sufficiently clear that this is a timeline of memory operations, though it does not explicitly distinguish itself from the similarly named sibling 'memory_history'.

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

Usage Guidelines2/5

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

The description provides no guidance on when to use this tool versus siblings like `memory_recall` or `memory_history`, nor does it mention any alternative conditions. Usage context is only implied by the word 'View'.

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. 9 tool updatesv3.5.0
    • First observedmemory_assemble_context
    • First observedmemory_audit
    • First observedmemory_history
    • First observedmemory_ingest_session
    • First observedmemory_publish_shared
    • First observedmemory_recall
    • First observedmemory_remember
    • First observedmemory_remind_me
    • First observedmemory_timeline

TDQS

A3.6/5.0

Scored across 9 tools

Disambiguation4/5

Most tools target distinct memory operations: storing, retrieving, scheduling, auditing, and publishing. Some overlap exists between memory_recall (search) and memory_assemble_context (curated retrieval), but their descriptions make the intended use clear.

Naming Consistency5/5

All tool names follow a consistent memory_verb_noun pattern using snake_case (e.g., memory_ingest_session, memory_publish_shared). The one slight deviation is memory_remind_me, but it still fits the overall verb-first convention.

Tool Count5/5

Nine tools cover the core memory management lifecycle without feeling bloated. The set is well-scoped for a memory server, with each tool serving a clear and necessary function.

Completeness3/5

The server covers storing, retrieving, scheduling, sharing, and diagnostics, but lacks explicit update or delete/forget tools. Memory_history implies prior edits exist, yet no edit tool is exposed, which is a notable gap for a memory management system.

Maintenance

ActivityMaintained
ResponsivenessSlow

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