原型 · 预览版:收录官方注册中心里的全部服务器,每 30 分钟同步一次。使用 Google 登录即可撰写评价。
PopNx MCP为使用 AI 的人准备的 MCP 服务器 · 用大白话讲清楚

KIP

作者:ldclabs版本 1.0.1i★ 91i在你的电脑上运行
注册名称io.github.ldclabs/KIP

安装方法

概览

作者提供的说明(英文原文)

Knowledge Graphs to enable memory persistence, knowledge evolution, explainable interaction.

它能帮你做什么

我们还没有为它写通俗的介绍。下面是我们从注册中心读取到的事实。

  • 连接方式: 在你的电脑上运行
  • 传输方式: stdio
  • 分类: 搜索与 AI 记忆
  • 你需要填写:KIP_BACKEND_URL, KIP_API_KEY

安装

选择你使用的 AI 应用,然后复制设置。

通过 2/2 项检查 · 在你的电脑上运行

第一步:你用哪个 AI?

选择你用的应用,步骤会显示在下方。

可连接 Claude,但需要先准备

需要 KIP 的密钥(在开发者网站注册,可能收费) · 需要先在电脑上安装 Node.js(只需一次) · 仅限电脑上的 Claude 应用(Windows 或 Mac),网页和手机不可用

  1. 安装 Node.js

    只需一次,所有需要 Node.js 的服务器都能用

    打开 Node.js 下载页 ↗

  2. 注册并创建 KIP 的密钥

    打开开发者网站,注册账户并创建 API 密钥,下一步要用。可能收费。

    获取密钥的方法见下方开发者的 README

  3. 打开电脑版 Claude 应用的设置文件

    SettingsDeveloperEdit Config

  4. 粘贴这些设置并保存文件

    {
      "mcpServers": {
        "kip": {
          "command": "npx",
          "args": [
            "-y",
            "@ldclabs/kip-mcp-server"
          ],
          "env": {
            "KIP_BACKEND_URL": "your-value",
            "KIP_API_KEY": "your-value"
          }
        }
      }
    }

    只把密钥粘贴到应用的输入框里,不要粘贴到聊天中或发给任何人

  5. 退出 Claude 再重新打开

  6. 试一试

    用 KIP 告诉我你能帮我做什么,然后做一件给我看

    ✓ 如果 Claude 说正在使用 KIP,或回答中出现 KIP 的工具名称,就表示连接成功

菜单名称已于 2026年11月11日 对照 Anthropic 帮助页面核实 · 你的界面可能因版本略有不同

ChatGPT 暂时无法连接 KIP

KIP 需要安装在你的电脑上,而 ChatGPT 只能连接在网上运行的服务器

你可以改用这些方式

A · 使用能连接 KIP 的应用

  • 需要 KIP 的密钥(在开发者网站注册,可能收费) · 需要先在电脑上安装 Node.js(只需一次) · 仅限电脑上的 Claude 应用(Windows 或 Mac),网页和手机不可用
  • 需要 KIP 的密钥(在开发者网站注册,可能收费) · 需要先在电脑上安装 Node.js(只需一次)
  • 需要 KIP 的密钥(在开发者网站注册,可能收费) · 需要先在电脑上安装 Node.js(只需一次)
  • 需要 KIP 的密钥(在开发者网站注册,可能收费) · 需要先在电脑上安装 Node.js(只需一次)

B · 搜索与 AI 记忆 类中能连接 ChatGPT 的其他服务器

从同类中使用较多的里挑选,仅限用地址即可连接、无需密钥的

可连接 Claude Code,但需要先准备

需要 KIP 的密钥(在开发者网站注册,可能收费) · 需要先在电脑上安装 Node.js(只需一次)

  1. 安装 Node.js

    只需一次,所有需要 Node.js 的服务器都能用

    打开 Node.js 下载页 ↗

  2. 注册并创建 KIP 的密钥

    打开开发者网站,注册账户并创建 API 密钥,下一步要用。可能收费。

    获取密钥的方法见下方开发者的 README

  3. 打开终端

    Mac:按 Command+空格,输入 Terminal · Windows:打开 PowerShell

  4. 粘贴这条命令并按 Enter

    claude mcp add kip --env KIP_BACKEND_URL=your-value --env KIP_API_KEY=your-value -- npx -y @ldclabs/kip-mcp-server

    按 Enter 前,把“your-value”改成你的密钥

    只把密钥粘贴到应用的输入框里,不要粘贴到聊天中或发给任何人

  5. 在 Claude Code 中输入 /mcp

    应能看到 KIP 在列表中并显示已连接

  6. 试一试

    用 KIP 告诉我你能帮我做什么,然后做一件给我看

    ✓ 如果 Claude Code 说正在使用 KIP,或回答中出现 KIP 的工具名称,就表示连接成功

菜单名称已于 2026年11月11日 对照 Anthropic 帮助页面核实 · 你的界面可能因版本略有不同

可连接 Cursor,但需要先准备

需要 KIP 的密钥(在开发者网站注册,可能收费) · 需要先在电脑上安装 Node.js(只需一次)

  1. 安装 Node.js

    只需一次,所有需要 Node.js 的服务器都能用

    打开 Node.js 下载页 ↗

  2. 注册并创建 KIP 的密钥

    打开开发者网站,注册账户并创建 API 密钥,下一步要用。可能收费。

    获取密钥的方法见下方开发者的 README

  3. 点击这个按钮打开 Cursor

    在 Cursor 中打开

    Cursor 会请你确认安装,点击 Install

  4. 填写你的密钥

    在 KIP 的设置中,KIP_BACKEND_URL, KIP_API_KEY 还是空的,填入你的密钥并保存

    只把密钥粘贴到应用的输入框里,不要粘贴到聊天中或发给任何人

  5. 在 Cursor 中以 Agent 模式打开 AI 聊天

  6. 试一试

    用 KIP 告诉我你能帮我做什么,然后做一件给我看

    ✓ 如果 Cursor 说正在使用 KIP,或回答中出现 KIP 的工具名称,就表示连接成功

菜单名称已于 2026年11月11日 对照 Cursor 帮助页面核实 · 你的界面可能因版本略有不同

可连接 VS Code,但需要先准备

需要 KIP 的密钥(在开发者网站注册,可能收费) · 需要先在电脑上安装 Node.js(只需一次)

  1. 安装 Node.js

    只需一次,所有需要 Node.js 的服务器都能用

    打开 Node.js 下载页 ↗

  2. 注册并创建 KIP 的密钥

    打开开发者网站,注册账户并创建 API 密钥,下一步要用。可能收费。

    获取密钥的方法见下方开发者的 README

  3. 点击这个按钮打开 VS Code

    在 VS Code 中打开

    VS Code 会请你确认安装,点击 Install

  4. 填写你的密钥

    在 KIP 的设置中,KIP_BACKEND_URL, KIP_API_KEY 还是空的,填入你的密钥并保存

    只把密钥粘贴到应用的输入框里,不要粘贴到聊天中或发给任何人

  5. 在 VS Code 中以 Agent 模式打开 AI 聊天

  6. 试一试

    用 KIP 告诉我你能帮我做什么,然后做一件给我看

    ✓ 如果 VS Code 说正在使用 KIP,或回答中出现 KIP 的工具名称,就表示连接成功

菜单名称已于 2026年11月11日 对照 VS Code 帮助页面核实 · 你的界面可能因版本略有不同

其他支持 MCP 的应用(如 Windsurf、Zed、Gemini CLI)添加方式各不相同,请在应用列表中查看

查看所有支持 MCP 的应用 →

JSON 设置在下方“开发者专用”中

卡住了?问问正在用 KIP 的人💬 讨论 →
开发者专用:终端命令和 JSON 设置

安装在电脑上

Claude Code

claude mcp add kip --env KIP_BACKEND_URL=your-value --env KIP_API_KEY=your-value -- npx -y @ldclabs/kip-mcp-server

Claude Desktop (claude_desktop_config.json)

{
  "mcpServers": {
    "kip": {
      "command": "npx",
      "args": [
        "-y",
        "@ldclabs/kip-mcp-server"
      ],
      "env": {
        "KIP_BACKEND_URL": "your-value",
        "KIP_API_KEY": "your-value"
      }
    }
  }
}

Cursor (~/.cursor/mcp.json)

{
  "mcpServers": {
    "kip": {
      "command": "npx",
      "args": [
        "-y",
        "@ldclabs/kip-mcp-server"
      ],
      "env": {
        "KIP_BACKEND_URL": "your-value",
        "KIP_API_KEY": "your-value"
      }
    }
  }
}

VS Code (mcp.json)

{
  "servers": {
    "kip": {
      "type": "stdio",
      "command": "npx",
      "args": [
        "-y",
        "@ldclabs/kip-mcp-server"
      ],
      "env": {
        "KIP_BACKEND_URL": "your-value",
        "KIP_API_KEY": "your-value"
      }
    }
  }
}

需要你填写的值

  • KIP_BACKEND_URL 必填 env
    HTTP URL for the KIP backend endpoint
  • KIP_API_KEY 机密 env
    Your API key for the service

这些设置是根据作者在注册中心的条目生成的。如果 README 的写法不同,请以 README 为准。

一览

每周下载量i
62/周
许可证i
未注明许可证
连接方式
在你的电脑上运行
最新版本i
1.0.1
发布到注册中心i
2026年1月9日
代码最后更新i
2026年10月26日
作者
ldclabs
软件包
npm: @ldclabs/kip-mcp-server
注册状态
活跃

哪些应用可以使用它

依据这个服务器的连接方式和各应用自己的文档,检查于 2026年11月10日。

在你的电脑上运行,适用于

ClaudeGooseLM StudioMsty StudioCherry StudioCursorVS Code + GitHub CopilotVisual StudioWindsurf (Devin Desktop)ZedKiroClineContinueJetBrains AI AssistantClaude CodeCodex CLIGemini CLIOpenCodenodecodeWarpLibreChatRaycast

全部应用及其优缺点 →

它能做什么

它在你的电脑上运行;安装后,工具列表会显示在你的 AI 应用中。

基础检查i

我们检查的是任何人都能看到的信息;我们没有阅读代码。不适用的检查项不计入总数。 · 检查于 2026年11月11日

  • ✓ 通过

    作者与仓库一致

    注册名称已通过 GitHub 账户 ldclabs 验证,该账户拥有此仓库

  • ✓ 通过

    仍在维护

    代码最后更新于16天前

  • … 尚未检查

    有许可证

    我们还没有从 GitHub 读取这个仓库

  • – 不适用

    端点有响应

    它在你的电脑上运行,没有互联网端点

  • – 不适用

    使用 OAuth 登录

    在你的电脑上运行,改用本地设置

  • … 尚未检查

    工具描述中没有隐藏指令

    它在你的电脑上运行;我们只能读取互联网端点的工具列表

讨论

提问、分享技巧或报告无法使用。按“有帮助”数量排序。

还没有帖子,欢迎第一个提问或分享。

此处内容由用户撰写,不代表 PopNx 团队。

使用 Google 登录后发帖

用户评价

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用 Google 登录后写评价

与“搜索与 AI 记忆”中的其他服务器比较i

  • 星标数多于此分类中其他 5883 个服务器的 98%
  • 发布到注册中心的时间比此分类中其他 5883 个服务器的 2% 更新
  • 基础检查结果等于或优于此分类中其他 5883 个服务器的 100%

与注册中心里这个分类中所有活跃的服务器比较。只有当发布者拥有该仓库时,星标数才计入。

常见问题

KIP 安全吗?

它在 2 项适用的基础检查中通过了 2 项。我们没有阅读代码。在添加密钥或令牌之前,请先看看这个服务器要求哪些权限,并使用只具备所需权限的密钥。

它需要哪些密钥或代码?

你需要填写:KIP_BACKEND_URL, KIP_API_KEY

它在我的电脑上运行还是在云端?

它在你的电脑上运行。你的 AI 应用会在需要时启动它,数据不会经过作者的服务器(除非它自己调用在线服务)。

它还在维护吗?

GitHub 仓库最后更新于16天前,最新版本(1.0.1)于10个月前进入注册中心。

哪些应用可以使用它?

支持 MCP 的应用,例如 Claude Desktop、Claude Code、Cursor 和 VS Code。你的电脑上还需要有 Node.js。

有什么可以替代它?

同一分类中有:Ruflo, Context7, Metabase

替代选择

同一分类中其他作者的服务器,按星标数排序。

如何看卡片:↓ 每周下载量i★ GitHub 星标i三大支柱:评价 · 检查 · 使用情况i更新 = 最新版本发布到注册中心的时间i

Ruflo

Agent teams, swarms, memory and hooks for Claude Code and Codex, served over MCP.

作者:ruvnet广泛使用↓ 7.4万/周★ 8.1万今天更新

在你的电脑上运行

Context7

Up-to-date docs for AI agents

作者:upstash广泛使用↓ 125万/周★ 6.4万昨天更新

电脑或云端

Metabase

Lets AI clients search, explore, query, and visualize data in a Metabase instance.

作者:metabase广泛使用★ 5万6个月前更新

在云端运行

作者的 README

打开 README

按原文从作者的仓库复制。PopNx MCP 没有审查或背书其内容。HTML、图片和脚本已被移除。链接会离开本站。

KIP 2.0 — Cognitive State Protocol for Agent Memory

[English](./README.md) | [中文](./README_CN.md)

Status

Default development version: KIP 2.0 — normative draft. The repository root contains the current protocol and tooling. KIP v1.0-RC11 is frozen for historical reference and migration; changing the default does not upgrade existing deployments or declare the 2.0 protocol stable.

  • The scope is frozen. A new contract enters the draft only together with engine evidence; corrections, simplifications and new evidence do not need that gate (Specification Status).
  • Names are stable; revisions are digests. There is one memory package, kip://profiles/cognitive-memory@2.0.0, one general domain package, kip://domains/general@1.0.0, one standard projection policy, kip:memory-default, one standard strength policy, kip:strength-half-life-30d, and schema IDs urn:kip:2.0:schema:*. A draft revision is identified by its content digest; earlier draft packages are not retained.
  • Both reference engines pass the whole engine suite — 431 cases in 26 fixtures, none pending. The suite manifest records the verified engine revision.
  • No learning result is claimed. Engine runs, bounded models and structural tests are separate evidence from measured Brain behavior, which stays not_run until a Brain evaluation measures it.

What the latest revision changed, and how it was validated, is in the revision record.

Start here

The archived MCP server and Skill implement v1 interfaces; native v2 integration starts with the current Interface and wire contracts above.

An interactive version — pan, zoom, relationship tracing, five guided views — is a self-contained file: download it and open it in a browser.

The one idea

A memory that cannot separate *what was said* from *what is true* will eventually lie to its owner with total confidence. KIP 2.0 refuses to collapse three questions:

Meaning      what can be represented at all

Belief what the Brain currently accepts Authority who may read, write, project, or elevate

So a stored Proposition is truth-neutral. An Assertion carries one actor's stance toward it, with mode, confidence, validity and evidence. Belief is not a stored field but an Epistemic Projection computed at read time over the eligible assertions:

Proposition exists  ≠  Proposition is true  ≠  Brain accepts Proposition

Change never rewrites the past. Three kinds of change keep three different histories:

  • The world changed. One new Assertion from the time of the change. Temporal succession closes the actor's older value at that point, so a formerly true claim is ended rather than declared wrong, and historical queries still answer with it. Two inferences never succeed one another.
  • A claim was wrong. A new Assertion supersedes the old one, which stays on record.
  • The Brain misrecorded it. Recording repair invalidates the wrong extraction; the source bytes and everyone's stance stay as they were.

Time is stated as honestly as it is known: asserted_at is when a claim was made, not when it was recorded; a claim with no stated start began no later than that; and a coarse date is a time bound, not an invented instant. A preference is an ordinary claim that changes within its kind — there is no Preference type. Where claims of one slot still conflict, the standard memory policy kip:memory-default resolves them the same way on every engine: context specificity first, then first-person testimony, then recency.

Evidence, provenance and schema get the same treatment: Evidence and Activity are first-class elements rather than metadata bags, and schema lives in versioned, digest-pinned packages. A Brain that meets a relation the Profile lacks drafts it into the Space's draft vocabulary with DEFINE, which only adds; promoting a draft onto an installed symbol is a Schema migration that only a Principal holding manage_schema performs. Retrieval composes with belief in one query through the Search Pattern: search finds candidates, and BELIEF decides what is accepted.

The proactivity layer

A memory that only answers when asked is half a memory. The other half is what the Brain does when nobody is talking to it — and it must not be a cron job. KIP 2.0 gives that half four mechanisms, none of which decides policy on the Brain's behalf:

MechanismWhat it is
WatchDurable attention state. A declared condition under which a change — or the absence of one — deserves attention. Armed watches are evaluated against committed Change Envelopes: a delta watch fires on a matching committed change; a *silence* watch fires once due_at has passed and the evaluator has consumed the change stream through that deadline without a match — the clock alone proves nothing. Proactivity becomes a state differential rather than a blind schedule. A fired Watch grants nothing: it creates attention, never an action, and reaches the business Agent through the Memory Interface's attention recall.
action_gateAn Activity class recording what the gate decided — act, ask, defer, or silence — with the inputs it weighed. Restraint is the hardest thing to justify after the fact, so deliberate silence is written down like any other outcome.
LIST DEPENDENTSBounded reverse traversal of provenance. Revise a root and the cognition compiled from it — insights, preference summaries, skills, the self-model — becomes discoverable in one operation instead of quietly stale. §57.5 makes the rule explicit: a revised root must not auto-retract its dependents, and must leave them reviewable. Whether one survives is a review decision, not a protocol rule.
PURGE PAYLOADDestroy an Evidence element's observed bytes while keeping the record: its digest, class, observation time, source, and the citations that depend on it. Data minimization that costs no provenance — corroboration and independence counting keep working on the surviving digest. Distinct from element purge, which destroys the record itself.

Supporting state lives in the Cognitive Memory Profile: WorkingState is the consolidated resume digest stamped with its basis_seq, so a Brain wakes from compiled state plus a delta rather than re-reading scrollback; computed dependency validity makes a revised root visible at the next read; and MnemonicState.utility holds the admission bet — how useful this memory is expected to be — kept deliberately separate from salience, from memory_strength, and from epistemic confidence. Strength itself is computed at read time from a base, an anchor and a pinned decay policy; nothing sweeps it, and reading never reinforces it.

The consequence channel

Everything above makes the system watch the world better. The consequence channel is how the world watches back.

Outcome Evidence records what actually happened after a decision, action, or trialed procedure — written by instrumentation (telemetry, verifiers, test harnesses, human review), never by the actor whose action it grades. An actor's own account is agent_statement, citable as context only; the separation is a conformance invariant, enforced as auditability — engine origin always records who wrote what, and Governance can restrict who may write outcomes — because an open protocol can make self-grading visible even where it cannot make it impossible.

Each outcome carries a task family that selects candidate consequences. Sharing the family establishes neither attribution nor baseline membership: under the optional Validated Learning companion, TrialRecord explicitly freezes comparable baseline attempts and outcomes. Treatment observations link through the instrument's outcome_observation Activity to an attempt and the action_gate decision that applied the exact Skill revision. Its DecisionRecord distinguishes retrieval from actual use, and grading counts independent attempts assigned before execution. A SkillRevision must name its task family before trial; a pattern that nothing could prove wrong is not procedural memory. On the channel sits the Skill lifecycle:

proposed → trialed → adopted → revoked

Lifecycle changes commit with a validated immutable EvaluationRecord on a lifecycle_verdict Activity and a guarded update (Validated Learning §7). Auditors replay exact inputs against the immutable TrialRecord; a Skill's current_trial only selects it, and its GradingState is a computed view. Without the companion, Skills stay unproven candidates — useful, recallable, never promoted. Only trialed → adopted promotes through a comparative verdict, and revocation is never harder than adoption. Same-state monitoring may retain standing under authorized policy without claiming new improvement; policy withdrawal may have zero outcom

只显示了开头部分;其余内容请在仓库中阅读