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Mnemosyne

Mnemosyne

by M4F-S

Mnemosyne 🧠

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

面向生产环境、本地优先的自主 AI 智能体分层记忆引擎。

Mnemosyne 为 AI 智能体(Hermes、OpenClaw、OpenManus、Claude Desktop、Cursor、Windsurf、CrewAI、LangChain)提供永久性的、结构化的长期记忆。它连接了人类可读的 Obsidian Markdown Vault 与高速 PostgreSQL + pgvector (HNSW) 或零配置 SQLite,支持混合倒数排名融合(RRF)搜索、wikilink 知识图谱、艾宾浩斯时间衰减、跨智能体舰队共享,以及异步 Google Drive 云同步。


📑 目录


Related MCP server: Synapto

⚡ 完整功能矩阵

功能

描述

优势

🤖 MCP 原生(v2024-11-05)

标准化的 stdio JSON-RPC 协议服务器

无缝集成 Claude、Cursor、Windsurf、Hermes、OpenClaw

🔎 高召回率 HNSW 向量搜索

pgvector HNSW 索引,使用 vector_cosine_ops(m=16, ef_construction=64)

无需聚类重训练即可实现亚毫秒级向量召回

⚖️ 混合 RRF 检索

稠密嵌入(1.0)+ GIN FTS(0.8)+ 图谱(0.6)+ 显著性(0.2)的倒数排名融合

同时捕获精确技术关键词(CVE、代码令牌)与模糊语义

🏛️ Wing 与 Room 作用域

2 级分类体系(wing = 领域/项目,room = 频道/主题)

消除上下文窗口膨胀与跨领域幻觉

🌐 跨智能体共享记忆

可跨多智能体舰队查询的中央 shared_db,带凭据筛查

集体舰队智能,同时不损害私有数据库

☁️ 异步 Google Drive 同步

本地优先的双向同步引擎,带 MD5 差异比对与 .conflict.md 分支解决

本地亚毫秒级智能体 I/O + 自动云备份与团队共享

⏳ 艾宾浩斯时间衰减

指数衰减 $Salience_t = Salience_0 \times (0.95)^{\Delta t}$,90 天自动归档

自动修剪瞬时噪声,同时保持活跃记忆清晰

📌 置顶记忆免疫

通过 pinned=True 或 #pinned 标签获得永久免疫衰减

确保基础指令和核心规则永不褪色

📖 Obsidian Zettelkasten

写入带 YAML frontmatter 与 [[Wiki Links]] 的人类可读 Markdown 笔记

可在 Obsidian 中直接可视化检查、编辑和图谱展示

📜 感知轮次的摄取器

1,500 字符滑动窗口分块,沿轮次边界带 200 字符重叠

保留完整对话历史,不破坏代码块

🛡️ 提示注入防护

中和控制令牌(`<

im_start

>、[INST]`);转义 XML 上下文标签

防止记忆投毒与上下文劫持攻击

⚡ FastEmbed ONNX 支持

超轻量 ONNX 运行时嵌入引擎(约 30MB 内存)

相比重型 PyTorch sentence-transformers 内存减少 90%

🔄 零配置 SQLite 回退

PostgreSQL 离线时自动回退到本地 SQLite

独立开发者工作站 100% 功能对等


🏗️ 系统架构

                                ┌──────────────────────────────────────────────┐
                                │          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 │
                                                 └─────────────────────────────────────────────┘

🧠 核心能力深度解析

1. 分层 Wing 与 Room 分类体系

记忆交叉污染是多智能体舰队中的主要故障模式。Mnemosyne 将记忆结构化为 2 级记忆宫殿:

  • wing(领域/项目): 顶级领域边界(例如 ecommerce、pentest、devops、shared)。

  • room(主题/频道): 细粒度主题分区(例如 database、firewall、stripe_api)。

查询可以严格限定在特定 wing 或 room 内,防止营销提示词召回渗透测试发现。

2. 混合倒数排名融合(RRF)搜索

标准向量搜索在精确技术字符串(例如 CVE-2024-38077、0x7fff5fbff8c0)上会失败,而关键词搜索在语义概念上会失败。Mnemosyne 执行多候选检索,并使用加权 RRF 合并结果:

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

  • 稠密 HNSW 向量搜索: 权重 $1.0$(384 维嵌入上的余弦距离)。

  • PostgreSQL 全文搜索: 权重 $0.8$(tsvector,标题权重为 A,内容权重为 B)。

  • 递归图谱遍历: 权重 $0.6$(递归 CTE 发现 1 跳和 2 跳 [[Wiki Links]])。

  • 记忆显著性引擎: 权重 $0.2$(重要性分数从 $0.0$ 到 $1.0$)。

3. 跨智能体共享记忆层(shared_db)

在自主多智能体环境中,智能体维护隔离的私有数据库(toy_db、old_db、candy_db 等)以防止状态损坏。然而,集体智能需要共享全局策略和经过验证的事实。

  • 发布: 使用 memory_publish_shared,经过审查的笔记被发布到 shared_db。

  • 凭据筛查: 内容会针对严格的 regex 过滤器进行扫描,以检测 Anthropic 密钥(sk-ant-)、Google Gemini 密钥(AIza...)、HuggingFace 令牌(hf_...)、OpenAI 密钥(sk-proj-...)、AWS 访问密钥(AKIA...)、Slack 令牌(xox-)和私钥。

  • 软失败召回: 当智能体查询记忆时,memory_recall 同时查询私有存储和 shared_db。如果共享数据库暂时不可达,它会优雅降级,不会中断智能体。

4. 艾宾浩斯时间衰减与置顶免疫

记忆会随时间自然失去相关性。Mnemosyne 实现了赫尔曼·艾宾浩斯的指数遗忘曲线:

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

  • 触达反馈: 访问记忆会更新 last_accessed_at,重置其衰减。

  • 夜间自动归档: 整合过程自动将 $\text{Salience} < 0.05$ 且超过 $>90\text{ 天}$ 未访问的笔记转换为 status = 'archived'。

  • 置顶免疫: 系统规则、核心策略或标记为 pinned=True 或带有 #pinned 标签的笔记获得对时间衰减的永久免疫($\text{Salience} = 1.0$)。

5. Obsidian Markdown Vault 与双向图谱

智能体创建的每条记忆都会同时作为人类可读的 .md 文件写入你的 Obsidian vault 中:

  • Zettelkasten Frontmatter: 包含 title、date、tags、type、salience、wing 和 room。

  • 知识图谱: 以 [[目标笔记]] 形式提及的目标笔记会被自动解析为 PostgreSQL 中的双向边。

  • 实时检查: 在桌面或移动设备上打开 Obsidian,在 Obsidian 的交互式图谱视图中探索你的智能体舰队的集体记忆。

6. 感知轮次的逐字会话摄取器

对话记录通常包含在有损摘要中丢失的关键细微差别。memory_ingest_session:

  • 沿轮次边界(User:、Assistant:、### Turn、**Human**:)拆分原始记录。

  • 对于超过 1,500 字符的轮次,应用线性滑动窗口(1,500 字符,200 字符重叠)。

  • 使用 [[Session ... Turn 01 Part 02]] wikilink 链接连续分块,保留代码块、执行轨迹和对话流程。

7. 异步 Google Drive 云同步

让你的智能体 vault 在多台机器或移动设备上安全备份和同步:

  • 本地优先速度: 代理工具调用以本地 SSD 速度(<1ms)执行,不会因 Google Drive 网络延迟而阻塞。

  • 后台守护进程 / Cron 同步: 扫描 vault 文件,计算 MD5 校验和,并与 Google Drive 双向同步增量。

  • 冲突解决: 如果文件同时在 Google Drive 和本地代理 vault 中被修改,Mnemosyne 会将传入版本保存为 NoteName.conflict-YYYYMMDD-HHMMSS.md,防止数据丢失。

  • 身份验证: 支持 Google Cloud 服务账号 JSON(GOOGLE_APPLICATION_CREDENTIALS、GDRIVE_SERVICE_ACCOUNT_JSON)和 OAuth2 用户令牌(GDRIVE_TOKEN_JSON)。

8. 灵活的嵌入后端(FastEmbed / 微服务 / 本地)

Mnemosyne 可适应任何部署资源预算:

  1. FastEmbed ONNX Runtime(推荐用于独立节点): 使用 ONNX Runtime C++ 执行(约 30MB 内存)。零 PyTorch 开销。

  2. 集中式微服务(mnemosyne.embed_service): 在单个专用容器中托管 sentence-transformers,通过 HTTP(MEMORY_EMBED_URL)为多个代理容器提供服务。

  3. 本地 SentenceTransformers: 独立 PyTorch 执行(all-MiniLM-L6-v2,384 维)。

  4. 确定性哈希回退: 为零资源受限环境提供零内存数学向量哈希。

9. 纵深防御安全与注入防护

  • 路径遍历免疫: 双重解析的规范路径检查(is_relative_to)确保文件操作无法逃逸 vault 根目录。

  • 原子同级写入: 文件写入同级临时文件(.note.pid.tmp)并原子重命名,对 EXDEV 跨设备卷挂载自动回退。

  • 控制令牌中和: 中和散文中的 LLM 注入令牌(<|im_start|>、<|system|>、[INST]、<<SYS>>),同时逐字保留代码块。

  • 结构化 XML 上下文封装: 召回的记忆被包裹在 <recalled_memory_context id="..." title="..." source="..."> 标签中,并进行内部标签转义,确保宿主 LLM 永远不会将召回的记忆与活动系统指令混淆。


🛠️ MCP 工具参考(8 个工具)

全部 8 个工具通过标准 MCP JSON-RPC stdio 原生暴露给代理:

1. memory_remember

在 vault 中存储带语义嵌入、标签和分层作用域的私有记忆笔记。

{
  "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

将经过净化和审核的发现或策略发布到跨代理共享舰队记忆(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

使用混合 RRF、HNSW 向量、关键词或图在私有和共享舰队数据库中搜索记忆。

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

4. memory_ingest_session

按轮次边界逐字摄取并分块完整的对话记录。

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

5. memory_timeline

按时间顺序检查最近的记忆操作(remember、recall、remind、consolidate)。

{
  "limit": 20
}

6. memory_history

在更新前查看特定记忆笔记的版本历史和过往编辑快照。

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

7. memory_remind_me

安排未来的前瞻性提醒或周期性任务。

{
  "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

获取实时记忆健康指标、存储后端状态、请求计数和活动翼区。

{}

🌐 多代理舰队生产架构

在多代理生产环境中(例如 5 代理 Hermes 舰队),Mnemosyne 在内部 Docker 网络上隔离代理数据库,同时提供共享智能:

                                  ┌─────────────────────────────────────────┐
                                  │      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        │
                               └───────────────────────────────────┘

🚀 快速开始与安装

1. 安装

# 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. 使用 Docker Compose 运行

启动 PostgreSQL + pgvector 容器:

docker compose up -d

3. 启动 MCP 服务器

# 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

🤖 代理框架集成配方

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 与自主代理脚本

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"])

💻 完整 CLI 命令参考

Mnemosyne 包含功能完备的管理 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

⚙️ 环境变量参考

变量

默认值

描述

MEMORY_VAULT_PATH

~/.mnemosyne/vault

本地 Obsidian Markdown vault 目录的文件系统路径

MEMORY_DB_DSN

(无)

PostgreSQL DSN(例如 postgresql://user:pass@host:5432/db)。如果未设置,则使用 SQLite

MEMORY_SHARED_DSN

(无)

可选跨代理共享舰队数据库的 PostgreSQL DSN

MEMORY_AGENT_NAME

local-agent

多代理舰队部署中源代理的标识符

MEMORY_EMBED_URL

(无)

远程集中式嵌入微服务的 URL(例如 http://localhost:8000)

MEMORY_REQUIRE_POSTGRES

false

设置为 true 时,如果 PostgreSQL 失败则抛出错误而不是回退到 SQLite

GOOGLE_APPLICATION_CREDENTIALS

(无)

用于 Google Drive 同步的 Google Cloud 服务账号 JSON 文件路径

GDRIVE_SERVICE_ACCOUNT_JSON

(无)

Google Cloud 服务账号凭据的字符串化 JSON 内容

GDRIVE_TOKEN_JSON

(无)

已授权 Google OAuth2 用户令牌的字符串化 JSON 内容

TOKENIZERS_PARALLELISM

false

禁用 HuggingFace 分词器分支以保持 stdio JSON-RPC 流完整性

HF_HUB_DISABLE_PROGRESS_BARS

1

禁用 stdio 中的进度条以保持 MCP 流纯净

HF_HUB_OFFLINE

0

设置为 1 可使用本地缓存 100% 离线运行 SentenceTransformers

TRANSFORMERS_OFFLINE

0

设置为 1 可防止 transformers 发出外部 HuggingFace 网络请求


🧪 测试与基准

Mnemosyne 维护全面的测试套件(单元测试、安全注入测试、SQLite 测试和实时 PostgreSQL pgvector 集成测试):

# 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

📄 许可证

Apache-2.0 许可证。为开放自主代理生态系统而构建。完整详情请参阅 LICENSE。

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

ActivityActive
ResponsivenessSyncing

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