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这是什么?

MCP Light Memory 是一个面向编码代理和 MCP 客户端(Warp、OpenCode、JetBrains AI Assistant / PyCharm、Claude Code、Cursor)的轻量级、本地优先的持久记忆系统。它充当 检查点 + 检索层 —— 存储跨会话恢复复杂工作所需的最小持久状态,而无需将完整对话保留在模型的上下文窗口中。

当你的代理开始一个任务时,它会调用 context 并取回相关的历史决策、陷阱、约束和假设——经过排序、去重并受信任边界约束。当任务完成时,它会将工作状态保存为检查点。下一次会话,即使重启之后,记忆依然存在。

Related MCP server: mcp-chest-memory

为什么使用它?

问题

MCP Light Memory 的解决方案

代理在会话之间忘记一切

Markdown 文件持久化在磁盘上;代理通过 BM25 + 可选嵌入检索它们

完整会话历史对上下文来说太大

只检索相关的记忆(受 token 预算限制、经 MMR 多样化)

云依赖 / 隐私问题

100% 本地、离线、零网络调用、无守护进程

繁重的设置 / 依赖

零必需运行时依赖(纯 Python 3.8+ 标准库);可选的 sentence-transformers 用于更好的语义检索

通过存储的记忆进行提示注入

每条检索到的记忆都明确标记为 trust: untrusted 证据,并带有注入警告启发式规则(ADR-015)

多项目隔离

带注册表白名单的路由器、write:false 硬边界、每次调用的子进程隔离

MCP 协议漂移

双时代支持:现代 2026-07-28 + 旧版 2024-11-05…2025-11-25

工作原理(机制)

  • Markdown 是事实来源。 每条记忆都是一个带 YAML frontmatter(id、type、status、tags、sources、links、valid_from、valid_to、supersedes)的 .md 文件。人类可读、可 diff、持久化。

  • SQLite 是可重建的缓存。 BM25/FTS5 索引 + 可选的嵌入向量 + 使用跟踪。删除后一切都会从 Markdown 重建。

  • 检索: 纯 Python BM25 + 可选的稠密嵌入 → RRF 融合 → MMR 多样化 → 策略加权(类型/状态/时间)→ token 预算截断。自适应模式:先稀疏,仅在效果弱时使用稠密。

  • 生命周期: remember → update → supersede(双向链接,永不删除历史)→ forget(归档,永不删除)→ timeline(时间视图)。使用 search --at YYYY-MM-DD 进行历史查询。

  • 信任边界: 检索到的内容被包裹在 === BEGIN/END INTERNAL_RAG MEMORY === 中,并带有 SECURITY NOTICE 标头。结构化 JSON/MCP 携带 trust: untrusted + 可选的 security_flags: ["instruction_like_content"]。

  • 证据时效性: 每个结果都包含针对本地路径类证据的 evidence_state(present/missing/unverifiable)——在检索时派生,从不持久化。

  • 多项目路由器: 通过 JSON 注册表用一个 MCP stdio 服务器统一管理多个项目。在生成子进程之前,write:false 会阻止会修改数据的工具。每次调用子进程隔离(无共享状态)。

安装

前提条件

  • Python 3.8+(使用 py 启动器、python 或 python3——安装程序会自动检测真实的解释器,并拒绝 WindowsApps 存根)

  • Git(目标项目必须是 git 仓库)

  • 可选:pip install sentence-transformers numpy 以获得更好的语义检索

当前版本由 VERSION 文件定义——请查看它(或运行 mlm.py --version),而不是硬编码一个预期的版本号。

快速开始

克隆此仓库一次,然后安装到任意项目:

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

安装程序会:

  • 复制技能文件 + 创建 INTERNAL_RAG/ + AGENTS.md

  • 运行 init + checkpoint + validate(因此 guard 会立即显示 OK)

  • 在能够安全操作时,将 MCP 服务器自动注册到客户端配置中(或报告 MANUAL_REQUIRED / 打印 JetBrains 操作说明)

  • 将经验证 Python 解释器的绝对路径写入配置(可避开 Windows PATH 问题)

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

安装矩阵

一个安装程序,四种客户端,两种配置范围。完整指南:docs/INSTALLATION.md。

客户端

项目范围

全局范围

Warp(自动写入配置;项目激活可能需要批准)

install.py . --client warp

install.py . --client warp --global

OpenCode stable (V1)(安全 JSON 配置写入自动完成)

install.py . --client opencode

install.py . --client opencode --global

OpenCode 2 (V2, beta)(安全 JSON 配置写入自动完成)

install.py . --client opencode2

install.py . --client opencode2 --global

JetBrains AI / PyCharm(在 IDE 界面中手动操作)

install.py . --client jetbrains

install.py . --client jetbrains --global

  • --global 改变客户端配置的作用范围(~/.warp/.mcp.json 与 {repo}/.warp/.mcp.json、~/.config/opencode/opencode.json 与项目 opencode.json)。服务器仍然指向你安装时的目标项目。

  • 需要为多个仓库提供一个全局 MCP 端点? 请使用多项目路由器——docs/MCP-MULTI-PROJECT.md。

  • JetBrains/PyCharm 是辅助式而非全自动:安装程序会准备好 JSON 和 Working Directory;你需要在设置 → 工具 → AI Assistant → MCP 中添加服务器,并选择服务器级别 = Project 或 Global。

  • 各客户端的手动设置(不使用安装程序):docs/INSTALLATION.md + 客户端页面(Warp · OpenCode)。

零配置:面向 Warp 和 OpenCode 的复制粘贴提示词

你可以将其中一条直接粘贴到客户端代理中。将 C:\Projects\App 替换为真实的目标仓库路径。

Warp —— 为单个项目安装:

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

Warp —— 单个项目的全局客户端配置:

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

OpenCode —— 为单个项目安装(stable/V1):

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

OpenCode —— 单个项目的全局客户端配置(stable/V1):

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

对于 OpenCode 2 / V2,使用相同的提示词,但明确说明 OpenCode 2 / V2,并要求使用 --client opencode2。更多变体:docs/ZERO-SHOT-SETUP-PROMPTS.md。


配置详情

Warp

Warp 从 ~/.warp/.mcp.json(全局,自动启动)或 {repo}/.warp/.mcp.json(项目,需要根据 Warp 文档 手动切换)读取 MCP 服务器配置。格式:mcpServers.<name>,包含 command、args、working_directory(始终设置它——记忆存储会从中解析)。参见 examples/warp.example.json 和 docs/WARP-SETUP.md。

OpenCode stable (V1)

OpenCode 读取项目根目录下的 opencode.json/.jsonc,或全局的 ~/.config/opencode/opencode.json。V1 服务器在 mcp.<name> 下是扁平结构(没有 servers 子键),带有 enabled: true,且 command 是一个数组——参见 examples/opencode-legacy.example.json 和 docs/OPENCODE.md。

OpenCode 2 (V2, beta)

相同的配置文件,不同的结构:mcp.servers.<name>,command 是一个数组,并且没有 enabled 字段(V2 通过 disabled: true 来禁用)——参见 examples/opencode-v2.example.jsonc 和 docs/OPENCODE.md。

JetBrains AI Assistant / PyCharm

PyCharm 不会自动读取任何 MCP 配置文件。安装程序会打印可直接粘贴的 JSON 和 Working Directory;你需要在设置 → 工具 → AI Assistant → MCP (STDIO) 中添加服务器,并选择服务器级别 = Project 或 Global。参见 examples/jetbrains.example.json。


多项目路由器

一个 MCP 连接统一管理多个项目——注册表白名单、write:false 硬边界、每次调用子进程隔离。

注册表文件(projects.json)

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

路由器的 Warp 配置

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

详见 docs/MCP-MULTI-PROJECT.md。


工作流

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

核心命令(CLI 别名:mlm.py 或旧版 irag.py):

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

路径映射(品牌重塑:internal-rag → MCP Light Memory)

新名称

旧路径(为兼容性保留)

MCP Light Memory(产品)

internal-rag(已弃用的产品名称)

mlm / mlm.py(主要 CLI)

irag.py(旧版别名,仍然可用)

mcp-light-memory(MCP 服务器名称)

internal-rag(旧版,仍在配置中有效)

mcp-light-memory-router(路由器名称)

internal-rag-router(旧版)

INTERNAL_RAG/(存储文件夹——未更改)

—

.agents/skills/internal-rag/(技能目录——未更改)

—

磁盘上的 INTERNAL_RAG/ 文件夹和技能目录 .agents/skills/internal-rag/ 有意保留其旧名称,以实现零迁移的向后兼容。参见 docs/MIGRATION-TO-MCP-LIGHT-MEMORY.md。

持久记忆(CRUD)

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

类型:decision、knowledge、constraint、gotcha、failure、hypothesis、session。

任务栈(中断)

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

配置(.irag.yml,可选)

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

mlm.py config 显示生效的配置。mlm.py config --init 写入一个模板。

可选嵌入(更好的检索)

pip install -r requirements-optional.txt

当该包可用且 .irag.yml 中设置了 embeddings: auto(默认)时,检索会使用嵌入,并在不可用时回退到 BM25。可在运行时通过 --embeddings on|off|auto 覆盖。

两种检索配置(参见 docs/EMBEDDINGS.md):

  • english-fast(默认,all-MiniLM-L6-v2)

  • multilingual(intfloat/multilingual-e5-small)——适用于波兰语-英语项目

离线 / 隔离网络

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

详见 docs/OFFLINE.md。

隐私与 Git

默认安装模式是仅本地。安装程序使用 .git/info/exclude,而不是项目的 .gitignore,这样本地记忆和集成文件就不会被意外提交。

在发布项目之前:

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

预期结果:RESULT: PASS

从项目中完全移除

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

卸载程序会在仓库外部创建备份,然后移除 INTERNAL_RAG 及其集成。使用 --keep-memory 可保留记忆数据。

文档

目标项目中的结构

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

事实来源

  1. 当前用户指令,2. 当前代码/测试/配置,3. 规范/ADR,4. 已验证记忆,5. 会话笔记,6. 假设。

记忆可能已过期。以代码为准。

许可证

MIT.


更新日志

1.8.0 — JetBrains 手动设置

  • --client jetbrains 不再写入虚假的配置文件(PyCharm 会忽略 MCP 配置文件)。改为打印可直接粘贴的 JSON 以及 IDE 菜单操作说明。

  • --unregister --client jetbrains 会打印一条提示,提醒在 IDE 界面中移除。

1.7.2 — JetBrains cwd + 客户端专属消息

  • JetBrains:将 working_directory 作为提示写入,并打印 WARNING,其中包含需要在 Settings → Tools → AI Assistant → MCP 中设置的确切路径。

  • 客户端专属的重启消息(Restart PyCharm / Restart Warp / Restart OpenCode)。

  • 安装输出中会打印 Memory store: <path>,便于立即验证。

1.7.1 — Windows Python 存根修复

  • detect_python() 会拒绝 WindowsApps 的 0 字节存根;优先使用 py -0p;并用 --version 验证每个候选。

  • 注册后验证:写入配置后立即运行 --version,并报告 PASS/FAIL。

  • --unregister 会删除空的配置文件及父目录(修复失效的 .warp/.mcp.json 空壳 → GUARD STALE)。

1.7.0 — 品牌更名为 MCP Light Memory

  • 从 internal-rag 全面更名为 MCP Light Memory(mcp-light-memory)。新增 CLI 别名 mlm(mlm.py)。标志/图标资源。迁移文档。GitHub 品牌更名清单。

  • 向后兼容:irag.py、INTERNAL_RAG/ 以及旧的 MCP 服务器名称保留为弃用别名。

  • 18 项品牌更名一致性测试。

1.6.1 — v1.6 后续加固

  • 变更/生命周期基准测试(11 个场景)。信任边界(ADR-015):trust: untrusted + security_flags。证据新鲜度(ADR-016):evidence_state。规模基准测试(100/1k/10k)。路由器安全回归(+12 项测试)。文档一致性测试。249 项测试通过。

1.6.0 — 检索质量 + MCP 2026-07-28

  • 记忆质量基准测试(37 个用例)。MCP 2026-07-28 双时代(server/discover、_meta、structuredContent、outputSchema)。注册表严格 write。分块前缀中的来源信息。自适应检索。链接感知上下文。consolidate --prepare。路由器延迟基准测试。ADR-010…016。

1.5.0 — 弃权门控 + 多项目路由器

  • 相关性/弃权门控(--meta)。FTS5 候选预过滤器。多项目 MCP 路由器。MCP 协议加固(纯 stdout、经 SDK 验证)。168 项测试。

1.4.0 — 分块 + 去重 + 时间生命周期

  • 感知章节的分块(schema v3)。SimHash 去重。多语言 PL/EN 配置。时间生命周期(valid_from/valid_to/supersedes/--at)。consolidate --dry-run。

1.3.0 — 持久化嵌入缓存

  • SQLite 中的分块级 float32 BLOB。多个模型可共存。index --vacuum/--embed-missing。

1.0.2 — Token 预算 + 隐私

  • Token 预算强制执行。过期记忆检测。重复检测。写入时隐私扫描。自动检查点定时器。离线/气隙隔离包。

1.0.0 — 初始版本

  • BM25 + MMR 检索。完整的记忆 CRUD。任务栈。MCP 服务器(JSON-RPC stdio)。Git 钩子。诊断。导出/导入。Token 预算。

Available Tools

8 tools
checkpointSave Task CheckpointA

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

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

TDQS

A4.4/5.0
Behavior4/5

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

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

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

Conciseness5/5

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

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

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

Completeness4/5

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

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

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

Parameters3/5

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

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

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

Purpose5/5

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

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

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

Usage Guidelines5/5

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

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

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

contextLoad Task ContextA

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

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

TDQS

A4.4/5.0
Behavior4/5

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

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

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

Conciseness5/5

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

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

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

Completeness4/5

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

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

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

Parameters3/5

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

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

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

Purpose5/5

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

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

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

Usage Guidelines5/5

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

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

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

guardCheck Checkpoint FreshnessA
Read-onlyIdempotent

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

NameRequiredDescriptionDefault

No parameters

Output Schema

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

TDQS

A4.4/5.0
Behavior3/5

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

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

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

Conciseness4/5

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

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

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

Completeness5/5

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

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

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

Parameters4/5

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

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

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

Purpose5/5

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

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

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

Usage Guidelines5/5

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

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

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

rememberStore Durable MemoryA

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

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

TDQS

A4.2/5.0
Behavior3/5

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

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

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

Conciseness5/5

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

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

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

Completeness4/5

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

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

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

Parameters3/5

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

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

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

Purpose5/5

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

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

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

Usage Guidelines5/5

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

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

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

resumeResume Saved TaskA

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

NameRequiredDescriptionDefault

No parameters

TDQS

A4.6/5.0
Behavior4/5

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

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

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

Conciseness5/5

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

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

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

Completeness4/5

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

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

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

Parameters4/5

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

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

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

Purpose5/5

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

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

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

Usage Guidelines5/5

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

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

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

statusInspect Memory StatusA
Read-onlyIdempotent

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

NameRequiredDescriptionDefault

No parameters

Output Schema

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

TDQS

A4/5.0
Behavior3/5

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

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

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

Conciseness5/5

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

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

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

Completeness5/5

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

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

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

Parameters4/5

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

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

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

Purpose4/5

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

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

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

Usage Guidelines4/5

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

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

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

tasksList Pending TasksA
Read-onlyIdempotent

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

NameRequiredDescriptionDefault

No parameters

Output Schema

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

TDQS

A4.1/5.0
Behavior3/5

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

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

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

Conciseness4/5

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

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

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

Completeness4/5

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

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

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

Parameters4/5

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

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

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

Purpose5/5

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

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

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

Usage Guidelines4/5

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

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

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

Tool Schema Changelog

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

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

TDQS

A4.2/5.0

Scored across 8 tools

Disambiguation4/5

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

Naming Consistency4/5

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

Tool Count5/5

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

Completeness4/5

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

Maintenance

ActivityStale
ResponsivenessNo issues

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