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。评价和账户功能将在后续阶段推出。
PopNx MCP为使用 AI 的人准备的 MCP 服务器 · 用大白话讲清楚

Context Fabric

作者:VIKAS9793版本 1.2.2i★ –i在你的电脑上运行
注册名称io.github.VIKAS9793/context-fabric

安装

概览

作者提供的说明(英文原文)

Context sync layer for AI agents. Ensures continuity and eliminates memory drift automatically.

PopNx MCP 的三大支柱

这三项永远不会合并成一个分数:意见、测量和人气是不同的东西。

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

安装

选择你使用的 AI 应用,然后复制设置。

安装之前

  • 基础检查:1 项中通过 1 项
  • 在你的电脑上运行,拥有你的用户账户的权限(文件、网络、应用)。
  • 注册中心没有列出需要填写的密钥。

你需要

  • npm已安装 Node.js(用于 npx 命令)

你用的是哪个应用?

在上方选择你的应用。我们会记住它,下次按钮会显示“安装到 …”。

打开 Claude Desktop → Settings → Developer → Edit Config,把下面的内容粘贴到 claude_desktop_config.json

{
  "mcpServers": {
    "context-fabric": {
      "command": "npx",
      "args": [
        "-y",
        "context-fabric"
      ]
    }
  }
}

在终端中运行这条命令

claude mcp add context-fabric -- npx -y context-fabric

在 Cursor 中打开

或者自己复制设置

粘贴到项目中的 .cursor/mcp.json,或 ~/.cursor/mcp.json

{
  "mcpServers": {
    "context-fabric": {
      "command": "npx",
      "args": [
        "-y",
        "context-fabric"
      ]
    }
  }
}

浏览器会先询问是否打开应用,然后应用会显示设置并请你确认。密钥的值会留空,由你自己填写。

在 VS Code 中打开

或者自己复制设置

粘贴到项目中的 .vscode/mcp.json

{
  "servers": {
    "context-fabric": {
      "type": "stdio",
      "command": "npx",
      "args": [
        "-y",
        "context-fabric"
      ]
    }
  }
}

浏览器会先询问是否打开应用,然后应用会显示设置并请你确认。密钥的值会留空,由你自己填写。

这个服务器在你的电脑上运行,而 ChatGPT 只能连接互联网上的服务器。请选择其他应用。

你的选择通过 Cookie 保存在这台设备上,无需账户。

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。

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7日

  • ✓ 通过

    作者与仓库一致

    注册名称已通过 GitHub 账户 VIKAS9793 验证,该账户拥有此仓库

  • … 尚未检查

    仍在维护

    我们还没有从 GitHub 读取这个仓库

  • … 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

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 7277 个服务器的 0%
  • 发布到注册中心的时间比此分类中其他 7277 个服务器的 24% 更新
  • 基础检查结果等于或优于此分类中其他 7277 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

Context Fabric 安全吗?

它在 1 项适用的基础检查中通过了 1 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?

注册中心没有列出必需的密钥,不过有些服务器会在首次使用时要求你登录。

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–,最新版本(1.2.2)于4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:Context7, Codebase Memory, Exa

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:★ 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

Context7

作者:upstash

Up-to-date docs for AI agents

电脑或云端★ 64,224MIT昨天更新
安装

Codebase Memory

作者:DeusData

Codebase knowledge graph for AI agents — 162 languages, sub-ms queries, 99% fewer tokens.

在你的电脑上运行★ 48,148MIT21天前更新
安装

Exa

作者:exa-labs

Connect AI agents to Exa for web search, content fetching, and multi-step research.

在云端运行★ 5,213MIT上个月更新
安装

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。

Context Fabric


Current stable release: v1.2.2. See CHANGELOG.md for the release notes.

Official Registry Support

Context Fabric is configured for official inclusion in the Model Context Protocol (MCP) Registry.

  • Namespace: io.github.VIKAS9793/context-fabric
  • Metadata: server.json
  • Distribution: Published as an npm package with official MCP Registry metadata.

The context synchronization layer for AI coding agents. Ensure project continuity, eliminate agent memory drift, and manage token budgets automatically across any coding session.

Context Fabric is an MCP server that automatically captures project state on every git commit, detects when stored context has drifted from codebase reality, and delivers structured, token-budgeted briefings to AI agents — without requiring any developer action.


The Problem

Developers using AI coding agents manually construct and maintain documentation systems, session handoff rituals, and context workflows to compensate for the absence of native project continuity tooling. These workarounds share one property: they go stale after commits, and nothing detects it.

The problem is not that AI forgets. The problem is context drift — stored context becomes incorrect after code changes, and AI agents proceed with that incorrect context confidently.

Context Fabric is the infrastructure layer that solves it at the root.


CADRE — Context-Aware Drift Resolution Engine

Context Fabric is powered by CADRE, a five-engine internal architecture. Every engine solves a specific, documented failure mode observed in real developer workflows.

What CADRE is. CADRE is the automated replacement for every manual context management system developers currently build themselves. It sits between the git event stream and the AI agent, deciding what context to capture, when it has drifted, which parts are relevant to the active query, what they cost in tokens, and how to deliver them structured and reliably.

Who CADRE is for. Developers building multi-session AI-assisted software projects who currently spend time on any of the following: updating markdown files after commits, creating session-end summaries, maintaining CLAUDE.md or AGENTS.md files, building RAG pipelines for project context, or manually deciding which documentation to load before each task.

The Five Engines

E1 — WATCHER

Installs a git post-commit hook on context-fabric init. Fires automatically on every commit. Reads committed Git blobs instead of the mutable working tree, computes SHA256 fingerprints, extracts exported symbols, calculates token estimates, and upserts everything into the local SQLite store. The developer does nothing after setup.

Replaces: markdown-per-commit workflows, 5-step session rituals, and AI interview workflows used to extract structured project knowledge before sessions.

E2 — ANCHOR

On every cf_query call, compares stored SHA256 hashes against the current state of every tracked file. Returns a DriftReport with a 0–100 drift score and severity classification: LOW (under 10%), MED (10–30%), HIGH (over 30%). Stale files are identified by path and hash delta.

Replaces: manual documentation alignment reviews and the undetected staleness in AGENTS.md that ETH Zurich research (arXiv:2602.11988) demonstrated degrades AI coding performance.

E3 — ROUTER

Runs SQLite FTS5 BM25 against the stored component index using the query text from cf_query. Returns components ranked by relevance. Path matches are weighted 2x over export symbol matches, because a file path carries structural meaning about the codebase organisation. Falls back to recency sort when the query produces zero MATCH results.

Replaces: manual selection of which documentation to include per task, and the modular context selection problem that emerges when projects decompose knowledge across many files.

E4 — GOVERNOR

Applies a configurable token budget ceiling to the E3 ranked output using greedy selection on the relevance-ordered list. Default: 8% of the model context window, leaving 92% for conversation and code. Oversize components are skipped so the remaining budget can still be used by later-ranked matches. Token estimates pre-calculated by E1 mean zero additional file reads at budget selection time.

Replaces: manual token management, and the pattern of keeping sessions alive indefinitely to avoid the cost of context restoration on restart.

E5 — WEAVER

Composes a structured markdown briefing from E3 and E4 output. Sections: Project State, Architecture, Architecture Decisions, Budget Summary. When E2 severity is MED or HIGH, a Drift Warning section is prepended before all other content, so the AI is informed of context reliability before reading it.

Replaces: static AGENTS.md briefings that never update, and the manual briefing preparation that developers perform at every session start.

Data Flow
git commit

| v E1 WATCHER --- SHA256 fingerprint --- SQLite upsert --- FTS5 index update | cf_query("...") | v E2 ANCHOR --- drift check --- DriftReport | v E3 ROUTER --- FTS5 BM25 --- ranked component list | v E4 GOVERNOR --- greedy selection --- budget-capped set | v E5 WEAVER --- briefing composition --- MCP response


Quick Start

npx context-fabric init

Initialises the SQLite store, installs the git post-commit hook, stages the stable local runtime under .context-fabric/runtime, and runs an initial capture. Connect the MCP server to your tool and context delivery is active from the next commit.

Installation & Setup

  1. Initialise your project: Navigate to any git-managed repository and run:
   npx context-fabric init
  1. Configure your AI Environment (Cursor/VS Code/Windsurf): Add the following to your MCP configuration file.

Windows Users (Crucial):

   {

"mcpServers": { "context-fabric": { "command": "cmd", "args": ["/c", "npx", "context-fabric"] } } }

Mac / Linux Users:

   {

"mcpServers": { "context-fabric": { "command": "npx", "args": ["-y", "context-fabric"] } } }

Health & Repair

Use the health commands when validating a local install or recovering from a broken hook/runtime state.

npx context-fabric doctor

npx context-fabric doctor --repair

doctor reports schema version, search index version, DB integrity, degraded mode, pending and failed captures, plus hook runtime readiness. doctor --repair refreshes the local runtime bundle, reinstalls the stable hook wrapper, validates .gitignore, and rebuilds the search index when the database is healthy.

Troubleshooting

spawn npx ENOENT Errors

This usually means npx is not in the system's inheritance path for the IDE.

  • Fix: Use the absolute path to node and the context-fabric binary.
  • Use where node (Windows) or which node (Mac/Linux) to find your path.
  • Example for NVM/FNM users:
  "command": "/Users/name/.nvm/versions/node/v22.14.0/bin/node",

"args": ["/Users/name/.nvm/versions/node/v22.14.0/bin/context-fabric"]

WSL Users

If you run VS Code on Windows but your code is in WSL, you must run init inside the WSL terminal and use the WSL-absolute path to node in your config.

Project Path Spaces

On Windows, spaces in your project path (e.g., C:\My Projects\app) can break the npx spawn. If the server fails, consider moving your project to a path without spaces.

Feedback & Reporting

If something breaks, please run:

npx context-fabric doctor

npx context-fabric diag remains available as a compatibility alias.


FAQ

Does Context Fabric send my code to a server? No. It is local-only and does not make outbound network calls for capture, retrieval, or drift analysis.

Does it index uncommitted changes? No. Captured state is based on committed Git objects. Uncommitted changes are detected by drift checks, but they are not indexed as the new source of truth until you commit and capture them.

Is this a replacement for `AGENTS.md`, `CLAUDE.md`, or human docs? It reduces the need for manual session-handoff documents, but it is not a replacement for product specs, onboarding guides, or stakeholder-facing documentation.

What files does it write locally? It writes under .context-fabric/ for the database and runtime bundle, and installs .git/hooks/post-commit to keep capture automatic.

When should I not use it? Do not use Context Fabric if you need cloud sync, cross-repo search, full semantic indexing of implementation bodies, or project-file mutation from MCP tools.

How do I recover a broken install? Run npx context-fabric doctor first. If the hook runtime or local state needs repair, run npx context-fabric doctor --repair.

Full FAQ: docs/FAQ.md


MCP Tools

ToolEnginesPurpose
cf_captureE1Manual context capture outside of a git commit
cf_driftE2Standalone drift check — returns severity and stale file count
cf_queryE2 + E3 + E4 + E5Full context briefing for the current task
cf_healthLocal healthReport database, capture, hook, and search index health
cf_log_decisionStoragePersist an architecture decision across sessions

All tools expose MCP annotations (readOnlyHint, destructiveHint, idempotentHint, openWorldHint) as of 1.1.0. cf_capture, cf_drift, cf_health, and cf_log_decision also return structuredContent alongside their text output; cf_query intentionally does not, since its output is prose meant for injection into an agent's context window rather than a

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