为你的编程代理打造的自动驾驶知识库。
安装一次。从那时起，你的代理就会在你编码的同时，自动为你构建一个结构化的项目知识库。无需记住提示词，无需调用 remember()，无需学习 API。
单个 Go 二进制文件。嵌入式 SQLite。零云端依赖。无 Docker。无 Python。无 Node 运行时。
Agent (Claude Code / Cursor / Kiro / Gemini CLI / ...)
↓ MCP stdio
mnemos serve
↓
Auto-compiled knowledge base (~/.mnemos/mnemos.db)每个内存服务器都能存储文本。而 mnemos 编译的是一个知识库。
当其他服务器期望你（或精心调整的提示词）来决定何时存储和何时检索时，mnemos 在后台运行完整的流水线：
Agent action → mnemos auto-pipeline:
├── Quality gate (reject/rewrite low-value content)
├── 3-tier dedup (hash → fuzzy → semantic)
├── Auto-summarize (extractive, fast; LLM if available)
├── File linking (extract identifiers, link to code)
├── Type classification (episodic / long_term / semantic / working)
├── Quality scoring (for retrieval ranking)
└── Decay scheduling (so knowledge base stays relevant)
Retrieval:
├── Hybrid search (FTS5 + optional semantic + RRF)
├── File-overlap boost (memories about active files rank higher)
├── MMR diversity (kill redundant results)
├── Adaptive packing (full content or summary based on budget)
└── Token-budget cap (always fits in context)你不需要调用任何这些功能。你的代理也不需要调用。钩子会在会话开始、提示词提交和会话结束时自动触发它们。
Related MCP server: Gingugu
第一层 — MCP 传输。 标准 MCP 服务器，stdio，适用于任何 MCP 客户端。
第二层 — 自动驾驶钩子。 一条命令 (mnemos setup claude) 即可连接钩子 + 引导 + MCP 配置。会话开始时自动注入相关上下文。提示词提交时自动根据主题变更进行搜索。会话结束时验证覆盖范围。
第三层 — 自动编译的知识库。 质量门控、三级去重、自动摘要、文件链接、MMR 上下文组装 — 全部自动化。你无需手动触发。包含被动后台守护进程，持续检测内存库中的陈旧信息、矛盾和缺失的关系。
Mem0 | Zep/Graphiti | engram | OMEGA | mnemos | |
原生支持 MCP | ✓ | ✓ | ✓ | ✓ | ✓ |
单个二进制文件，无运行时依赖 | — | — | ✓ | — | ✓ |
零云端 / 本地优先 | 部分 | — | ✓ | ✓ | ✓ |
一键式自动驾驶设置 | — | — | — | — | ✓ |
自动质量门控 | — | — | — | — | ✓ |
自动摘要 | — | — | — | — | ✓ |
自动文件链接 (感知 git) | — | — | — | — | ✓ |
MMR 上下文组装 | — | — | — | — | ✓ |
被动后台守护进程 | — | — | — | — | ✓ |
时间知识图谱 | — | ✓ | — | — | 部分 (衰减 + 覆盖) |
自托管成本 | $0-云端 | ~$50/月 (Neo4j) | $0 | $0 | $0 |
Mnemos 并不想成为 Zep — 它们的目标不同。如果你需要对业务事实进行时间推理并拥有企业级基础设施，Zep 是最佳选择。如果你是一名编程代理用户，想要一个能在笔记本电脑上自动运行的自动驾驶知识库，那么 mnemos 是最佳选择。
# Homebrew (macOS / Linux)
brew install s60yucca/tap/mnemos && mnemos setup claude
# curl (verify mnemos.dev is live before using)
curl -fsSL https://mnemos.dev/install.sh | bash && mnemos setup claude
# npm (coming in v1.2)
# npx mnemos setup claude
# Build from source (requires Go 1.23+)
git clone https://github.com/s60yucca/mnemos
cd mnemos && make build将 claude 替换为 cursor、kiro 或 gemini-cli。重启你的客户端。自动驾驶功能即刻运行。
mnemos setup <client> 会写入：
引导文件 (CLAUDE.md, .cursorrules, .kiro/steering/mnemos.md) — 告诉代理什么值得存储
钩子配置 (.claude/hooks.json 或等效文件) — 连接生命周期事件
MCP 配置 (.mcp.json) — 将 mnemos serve 注册为工具提供程序
三个钩子会自动运行：
会话开始 → mnemos hook session-start
在 Token 预算内组装相关记忆（MMR 多样化，文件加权）。注入到上下文中。冷启动 < 200 毫秒。
提示词提交 → mnemos hook prompt-submit
检测主题 + 意图变更。当变化有意义时，自动搜索知识库。遵守冷却时间以避免噪音。
会话结束 → mnemos hook session-end
验证是否捕获了持久性记忆。可选择存储最小化的面包屑信息。清理会话状态。
引导文件告诉代理什么值得记忆。钩子处理检索、去重、摘要、链接 — 这样代理就不必浪费 Token 去思考内存管理的逻辑。
除了钩子之外，mnemos 还运行一个后台守护进程，持续改进你的知识库：
陈旧检测 — 标记引用已删除文件或过时模式的记忆
矛盾检测 — 发现相互冲突的记忆
关系推断 — 自动链接相关的记忆
回填 — 为缺乏摘要的记忆追溯生成摘要
mnemos autopilot status # check daemon state
mnemos autopilot run # trigger immediate run
mnemos autopilot run --dry-run # preview findings without writing
mnemos autopilot report # view latest findings操作 | 350 条记忆 | 1,500 条记忆 |
| 57 毫秒 | 24 毫秒 |
| 55 毫秒 | 22 毫秒 |
| 42 毫秒 | 39 毫秒 |
| 27 毫秒 | 108 毫秒 |
hook session-start (冷启动) | < 200 毫秒 | — |
二进制文件大小 | ~12 MB | — |
硬件：M1 Pro, 16GB RAM, SSD 上的 SQLite。你的延迟可能会有所不同。
无论数据集大小如何，大多数操作都保持在 60 毫秒以下。钩子子命令使用 InitLight 模式 — 无后台工作进程，无会话中断。
价值基准测试（Token 节省、精度、避免陷阱）正在进行中。 请参阅 DOGFOODING_RUNBOOK.md 了解方法论。在公开发布之前，真实数据将替换此占位符。
工具 | 功能 |
| 存储记忆（完整自动流水线透明运行） |
| 混合 FTS + 语义 + 文件重叠搜索，带 MMR |
| 为会话开始组装预算感知、多样化的上下文 |
| 按 ID 获取 |
| 更新内容、摘要或标签 |
| 软删除（可通过 maintain 恢复） |
| 链接两条记忆 (supersedes, caused_by, depends_on) |
| 运行衰减、归档、GC、陈旧检测 |
# Agents call these automatically via MCP. You can also use directly:
mnemos store "JWT uses RS256, 1h expiry, config in auth/config.go"
mnemos search "token expiry"
mnemos stats
mnemos maintain大多数用户无需触碰此项。但如果你需要：
# ~/.mnemos/config.yaml
embeddings:
provider: noop # noop (default) | ollama | openai
# Pure FTS works fine. Enable semantic for meaning-based search.
quality_gate:
min_words: 5
max_words: 200
min_density: 0.3
require_specific: true # long_term memories need project identifiers
duplicate_threshold: 0.8
summarization:
extractive: true # always on, fast, offline
file_linking:
enabled: true # auto-disables outside git
hook:
enabled: true
search_cooldown: 5m
session_start_max_tokens: 2000
mmr_lambda: 0.7 # 0=max diversity, 1=max relevance
file_boost: 0.3
autopilot:
enabled: true
interval: 15m
contradiction_enabled: falseMnemos 会自动分类。可通过 --type 标志手动覆盖。
类型 | 衰减率 | 用途 |
| 快 (~1 天) | 待办事项、临时笔记、WIP |
| 中 (~1 个月) | 会话事件、Bug 修复 |
| 慢 (~6 个月) | 架构决策 |
| 非常慢 | 事实、定义、知识 |
| 快 | 活动任务上下文 |
我厌倦了每天早上都要向 Claude Code 重新解释我的项目。
我尝试过现有的内存服务器。它们大多数都能很好地存储文本。但每一个都期望我——或者一个精心调整的提示词——来决定何时存储和何时检索。那不是知识库。那只是一个带有 MCP 包装器的数据库。
Mnemos 是我为了实现真正的自动化而构建的。mnemos setup claude，重启编辑器，知识库就会自动编译。
mnemos setup claude # writes CLAUDE.md, .claude/hooks.json, .mcp.json
mnemos setup cursor # writes .cursorrules, .mcp.json
mnemos setup kiro # writes .kiro/steering/mnemos.md, .kiro/mcp.json
mnemos setup gemini-cli # writes GEMINI.md, .gemini/settings.json, .mcp.json标志：--global (为所有项目安装), --force (覆盖现有配置)。
mnemos init # first-time setup
mnemos store "..." # store (auto-pipeline)
mnemos search "auth" # hybrid search
mnemos list --project myapp # list memories
mnemos get <id> # fetch by id
mnemos update <id> --content "..." # update
mnemos delete <id> # soft delete
mnemos relate <src> <tgt> --type supersedes # typed relation
mnemos stats # storage + quality stats
mnemos maintain # decay + stale + GC
mnemos serve # MCP server (stdio)
mnemos version
# Autopilot setup
mnemos setup claude | cursor | kiro | gemini-cli [--global] [--force]
# Passive autopilot daemon
mnemos autopilot status
mnemos autopilot run [--dry-run] [--project <id>]
mnemos autopilot report [--project <id>]
# Backfill
mnemos backfill summaries --project <id> [--dry-run] [--limit N]
# Hook subcommands (called by clients, not manually)
mnemos hook session-start
mnemos hook prompt-submit
mnemos hook session-end不是聊天机器人内存 SaaS。 如需在客户支持机器人中进行用户偏好召回，请使用 Mem0。
不是时间知识图谱数据库。 如需对业务事实进行有效/无效推理，请使用 Zep。
不是云产品。 没有 mnemos 云。将来也不会有。
不是框架特定的。 原生支持 MCP。适用于任何支持 MCP 的工具。
Mnemos 只做一件事：为代理提供一个自动编译的知识库。
请参阅 ROADMAP.md。简要版本：
v1.1.1 (已发布): 完整自动流水线、MMR 上下文组装、文件感知检索、被动自动驾驶守护进程、基准测试框架
v1.2 (下一版本): 公共价值基准测试 (dogfooding)、npm 包装器、演示 GIF、HN 发布
v1.3 (计划中): 通过 git 实现团队记忆 — 共享 .mnemos/shared/ 以获取队友知识
v2.0+ (待定): 跨项目内存范围、内存压缩，由用户反馈驱动
MIT
Distill knowledge into a compiled article
| Name | Required | Description | Default |
|---|---|---|---|
| topic | Yes | Title/subject of the compiled article | |
| content | Yes | The compiled text | |
| project_id | No | Project scope | |
| source_ids | No | Comma-separated source memory IDs |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate destructive behavior, but the description does not explain what gets destroyed or any side effects. It adds no behavioral context beyond the annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, concise phrase with no wasted words. However, it may be too brief to be fully informative.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given 4 parameters, no output schema, and annotations indicating destruction, the description is too minimal. It fails to explain how parameters affect the compilation, what the return value is, or side effects.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema already defines parameters. The description does not add any additional meaning or usage hints beyond the field names and types.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The verb 'distill' is specific and the resource 'knowledge into a compiled article' is clear, but it does not differentiate from siblings like mnemos_store or mnemos_update, leaving ambiguity about what 'compile' entails compared to other tools.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
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 alternatives, nor does it mention prerequisites or when not to use it. Siblings are listed but not compared.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Assemble relevant context for a query within token budget
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes | Context query | |
| max_tokens | No | Token budget (default 4000) | |
| project_id | No | Project scope | |
| include_relations | No | Include related memories |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description claims 'assemble context', implying a benign read operation, but annotations indicate destructiveHint=true. The description fails to disclose that this tool may alter or delete state, creating a misleading impression.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single concise sentence that front-loads the primary action. It is appropriately sized for a simple tool, though it could include a bit more context without becoming verbose.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
With no output schema and 4 parameters, the description is too sparse. It does not explain what 'context' means, how relations are included, or what the return format is. The behavior around token budget and destructive side effects is not elaborated.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so parameters are already well-documented. The description adds no new semantic information beyond echoing 'query' and 'token budget'. Baseline score of 3 applies.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses the verb 'assemble' and resource 'relevant context', which clearly indicates the tool's purpose is to compile context for a query. It distinguishes from siblings like mnemos_search (which likely searches for specific items) and mnemos_store (which saves), but does not explicitly contrast them.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance on when to use this tool versus alternatives like mnemos_search or mnemos_get. The description does not mention prerequisites or conditions under which this tool is preferred.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Soft-delete a memory
| Name | Required | Description | Default |
|---|---|---|---|
| id | Yes | Memory ID |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The term 'soft-delete' adds value beyond annotations by indicating the operation marks data as deleted without immediate removal. However, no further behavioral details (e.g., reversibility, permission requirements, or state changes) are disclosed.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single phrase with no wasted words. It is front-loaded and efficient for its length.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a one-parameter tool with a soft-delete behavior and no output schema, the description is adequate but lacks depth—e.g., it does not clarify how a soft-deleted memory can be recovered or whether this affects search results.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so 'id' is already documented. The description adds no additional meaning beyond what the schema provides, earning a baseline score of 3.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description 'Soft-delete a memory' uses a specific verb ('soft-delete') and a clear resource ('memory'), making its purpose distinct from sibling tools like mnemos_store or mnemos_get. No ambiguity.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use soft-delete versus alternatives (e.g., permanent deletion, updates, or other operations). The description lacks context for choosing this tool over siblings.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Get a memory by ID
| Name | Required | Description | Default |
|---|---|---|---|
| id | Yes | Memory ID |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description describes a read operation ('Get'), but annotations set destructiveHint=true, implying mutation or deletion. This is a clear contradiction, and the description does not disclose any behavioral traits beyond what annotations provide.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single concise sentence with no fluff. However, it could be slightly more informative without losing conciseness, hence a 4 rather than 5.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
No output schema is provided, and the description does not explain the return format or structure of the memory object. For a retrieval tool, this is a significant omission, leaving the agent uncertain about what data will be returned.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% for the single required parameter 'id', with description 'Memory ID' already present in the schema. The description adds no extra semantic meaning beyond the schema, meeting the baseline but not exceeding it.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description 'Get a memory by ID' clearly states the verb (Get), resource (memory), and method (by ID). This distinctly separates it from sibling tools like mnemos_delete or mnemos_store, serving as a straightforward retrieval operation.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance provided on when to use this tool versus alternatives (e.g., mnemos_search). The description lacks any context about prerequisites, exclusions, or specific use cases.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Run decay, archival, and GC maintenance
| Name | Required | Description | Default |
|---|---|---|---|
| project_id | No | Project scope (empty = all) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The annotations already indicate destructiveHint=true, so the description adds some context by naming the specific maintenance operations (decay, archival, GC). However, it does not disclose what gets destroyed, whether changes are reversible, or other behavioral implications beyond the annotation.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, front-loaded sentence with no wasted words. Every part contributes to conveying the tool's purpose.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The description covers the basic purpose but lacks details about side effects, return values, or how the parameter affects execution. Given the absence of an output schema and the destructive nature, more context would be beneficial for safe and effective use.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100% for the single parameter, so baseline is 3. The description does not add any additional meaning to the parameter beyond what the schema already provides.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb 'Run' and specifies the resources 'decay, archival, and GC maintenance', which distinguishes it from sibling tools like mnemos_delete or mnemos_store. However, it could be more specific about what each maintenance operation entails.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
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 alternatives, such as when to run maintenance instead of using mnemos_delete or mnemos_update. No context about prerequisites or typical scenarios is given.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Create a relation between two memories
| Name | Required | Description | Default |
|---|---|---|---|
| strength | No | Relation strength [0.0, 1.0] | |
| source_id | Yes | Source memory ID | |
| target_id | Yes | Target memory ID | |
| relation_type | Yes | Relation type: relates_to|depends_on|contradicts|supersedes|derived_from|part_of|caused_by |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations indicate destructiveHint=true and readOnlyHint=false, but the description does not explain whether creating a relation overwrites existing ones or has side effects. The description adds minimal behavioral context beyond what annotations already provide.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single sentence with no wasted words, delivering the core purpose efficiently.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Despite having 4 parameters and no output schema, the description is very brief. It does not explain the effect of the relation, uniqueness constraints, or behavior on duplicates, leaving the agent with significant gaps.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, so the schema fully documents all parameters. The description adds no additional meaning to the parameters, meeting the baseline but not exceeding it.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool creates a relation between two memories, which distinguishes it from siblings like mnemos_store (store a memory) or mnemos_delete (delete). However, it could be more specific about what a relation entails in the memory graph.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use this tool versus alternatives, such as mnemos_context for contextual links. The description lacks context on prerequisites or exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Report the live MCP server runtime identity: version, host, pid, executable, uptime, data dir, and project scope
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Description indicates read-only behavior ('Report'), but annotations set destructiveHint: true, a direct contradiction. The description fails to disclose any actual destructive behavior.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Single sentence, front-loaded with the action and resource, no wasted words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Lists all expected return fields, adequate for a simple info tool. No output schema, so description covers main content, though format or example could strengthen it.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
No parameters, so baseline 4. Description adds context about what the tool reports (version, host, etc.), compensating for the lack of parameters.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
Description clearly states it reports runtime identity and lists specific items (version, host, pid, etc.). Distinct from sibling tools like mnemos_compile, mnemos_delete, etc., which have different purposes.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Implied usage as a diagnostic/info tool, but no explicit guidance on when to use it vs alternatives. No exclusions or prerequisites mentioned.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Search memories using hybrid text+semantic search
| Name | Required | Description | Default |
|---|---|---|---|
| mode | No | Search mode: text|semantic|hybrid (default hybrid) | |
| limit | No | Max results (default 10) | |
| query | Yes | Search query | |
| project_id | No | Filter by project |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description describes a read-only search operation, but annotations set readOnlyHint=false and destructiveHint=true, creating a contradiction. The description fails to clarify the actual behavioral traits.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, concise sentence that immediately states the purpose, with no wasted words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a search tool with no output schema, the description is brief and lacks details about return format, pagination, or behavior, though it covers the core functionality.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the description adds no extra meaning beyond the schema. Baseline of 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool searches memories and specifies the method (hybrid text+semantic search), distinguishing it from siblings like mnemos_get or mnemos_delete.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage for searching but does not explicitly compare to siblings like mnemos_get or mnemos_context, nor does it provide when-not-to-use guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Store a new memory in Mnemos
| Name | Required | Description | Default |
|---|---|---|---|
| tags | No | Comma-separated tags | |
| type | No | Memory type: short_term|long_term|episodic|semantic|skill|compiled | |
| source | No | Source identifier | |
| content | Yes | Memory content (1 byte to 100KB) | |
| summary | No | Optional summary | |
| category | No | Memory category | |
| project_id | No | Project scope |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description says 'Store a new memory' but annotations indicate destructiveHint=true, implying potential data destruction. The description does not address this contradiction or provide any side-effect context, such as overwriting behavior or resource implications.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
At 6 words, it is very concise but may be too brief for a tool with 7 parameters and no output schema. It front-loads the purpose but lacks supporting detail, making it adequately concise but not optimally informative.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity (7 params, no output schema), the description fails to mention return values, confirmation behavior, or the consequences of the 'destructive' annotation. It is incomplete for an agent to use effectively without additional context.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
All 7 parameters have schema descriptions (100% coverage), so the description adds no additional meaning. It does not elaborate on how parameters like 'type' or 'tags' affect storage behavior beyond the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb 'store' and resource 'memory' in 'Mnemos', indicating a creation operation. However, it does not differentiate from sibling tools like mnemos_update or mnemos_relate, leaving ambiguity about when to use this tool over others.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use this tool versus alternatives. There is no mention of prerequisites, exclusions, or context for choosing store over related tools like compile or update.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Update a memory (PATCH semantics)
| Name | Required | Description | Default |
|---|---|---|---|
| id | Yes | Memory ID | |
| tags | No | New comma-separated tags | |
| content | No | New content | |
| summary | No | New summary |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate destructiveHint=true, which aligns with 'Update'. The description adds 'PATCH semantics' but no extra behavioral details (e.g., what happens to unspecified fields, authorization needs). It is minimally adequate.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
A single sentence that is perfectly concise and front-loaded. Every word contributes essential information (action, resource, update semantics).
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a destructive tool with no output schema, the description lacks context about return values, side effects, or behavior on error. More detail would be needed for safe invocation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Input schema covers all 4 parameters with descriptions (100% coverage). The tool description adds no additional meaning beyond the schema, so baseline score of 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description 'Update a memory (PATCH semantics)' clearly states the action (update) and the resource (memory). The parenthetical note adds specificity about partial updates, distinguishing it from siblings like mnemos_store (create) or mnemos_delete.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance on when to use this tool vs alternatives like mnemos_get or mnemos_search. No prerequisites or exclusions are mentioned, leaving the agent without decision context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.
v1.2.1mnemos_runtimev1.1.14mnemos_compilemnemos_store1 field changedInput schema / properties / type / descriptionPrevious value: -"Memory type: short_term|long_term|episodic|semantic"New value: +"Memory type: short_term|long_term|episodic|semantic|skill|compiled"v0.1.2mnemos_relate1 field changedInput schema / properties / relation_type / descriptionPrevious value: -"Relation type"New value: +"Relation type: relates_to|depends_on|contradicts|supersedes|derived_from|part_of|caused_by"v0.1.0mnemos_contextmnemos_deletemnemos_getmnemos_maintainmnemos_relatemnemos_searchmnemos_storemnemos_updateEach tool has a clearly distinct purpose: CRUD operations (store, get, update, delete), search, maintenance, relation creation, context assembly, compilation, and runtime info. No two tools overlap in functionality, ensuring an agent can easily select the correct tool.
All tools follow a consistent 'mnemos_' prefix with an underscore-separated verb or noun. Most use imperative verbs (compile, delete, get, maintain, relate, search, store, update), while 'context' and 'runtime' are nouns. This minor inconsistency prevents a perfect score.
With 10 tools, the surface is well-scoped for a memory/knowledge server. It covers essential CRUD, search, maintenance, relations, and advanced features like compilation and context assembly without being overwhelming or sparse.
The tool set covers the full memory lifecycle (create, read, update, soft-delete) plus advanced operations (compile, context, relate, maintain, runtime). Minor gaps include missing batch operations or explicit undo for soft-delete, but the core domain is well-served.
Unclaimed servers have limited discoverability.
If you are the server author, to access and configure the admin panel.
Persistent memory for AI agents. Semantic search, memory graph, W3C DID identity.
Persistent memory for AI agents. Search and store durable facts, preferences and decisions.
Persistent, inspectable memory for AI agents with lineage, correction, and a hosted MCP endpoint.
Persistent memory for AI agents across Claude, ChatGPT and any MCP client.
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