Context7
Up-to-date docs for AI agents
作者による説明(英語の原文)
Context sync layer for AI agents. Ensures continuity and eliminates memory drift automatically.
この3つを1つのスコアにまとめることはしません。意見、測定、人気はそれぞれ別のものだからです。
このサーバーのやさしい解説はまだ書いていません。以下はレジストリから読み取った事実です。
使っているAIアプリを選んで、設定をコピーしてください。
必要なもの
どのアプリを使っていますか?
上でアプリを選んでください。選んだアプリを覚えておくので、次からはボタンに「…にインストール」と表示されます。
Claude Desktop → Settings → Developer → Edit Config を開き、claude_desktop_config.json にこれを貼り付けます
{
"mcpServers": {
"context-fabric": {
"command": "npx",
"args": [
"-y",
"context-fabric"
]
}
}
}ターミナルでこれを実行します
claude mcp add context-fabric -- npx -y context-fabricプロジェクトの .cursor/mcp.json、または ~/.cursor/mcp.json に貼り付けます
{
"mcpServers": {
"context-fabric": {
"command": "npx",
"args": [
"-y",
"context-fabric"
]
}
}
}ブラウザがアプリを開いてよいか確認し、そのあとアプリが設定を表示して確認を求めます。キーの値は空欄のままなので、自分で入力してください。
プロジェクトの .vscode/mcp.json に貼り付けます
{
"servers": {
"context-fabric": {
"type": "stdio",
"command": "npx",
"args": [
"-y",
"context-fabric"
]
}
}
}ブラウザがアプリを開いてよいか確認し、そのあとアプリが設定を表示して確認を求めます。キーの値は空欄のままなので、自分で入力してください。
これはパソコンで動くサーバーですが、ChatGPTがつなげるのはインターネット上のサーバーだけです。ほかのアプリを選んでください。
選んだアプリは、この端末のCookieに保存されます。アカウントは不要です。
この設定は、作者のレジストリ登録情報から作成しています。READMEと違う場合は、READMEに従ってください。
パソコンで動くサーバーです。インストールすると、ツール一覧がAIアプリに表示されます。
誰でも見られる情報をチェックしており、コードは読んでいません。当てはまらない項目は数に入れていません。 · 2026年11月7日にチェック
レジストリ名は、リポジトリを所有するGitHubアカウント VIKAS9793 で確認済みです
このリポジトリはまだGitHubから読み取っていません
このリポジトリはまだGitHubから読み取っていません
これはパソコンで動くため、インターネット上のエンドポイントはありません
パソコンで動き、代わりにローカルの設定を使います
パソコンで動くサーバーです。ツール一覧を読み取れるのは、インターネット上のエンドポイントがある場合だけです
レジストリでこのカテゴリにある有効なすべてのサーバーと比較しています。スターは、公開者がリポジトリの所有者である場合だけ数えます。
当てはまる基本チェック1項目のうち1項目に合格しました。コードは読んでいません。キーやトークンを追加する前に、サーバーがどんなアクセスを求めているかを確認し、必要な権限だけを持つキーを使ってください。
レジストリには必須のキーは載っていません。ただし、最初に使うときにサインインを求めるサーバーもあります。
あなたのパソコンで動きます。必要なときにAIアプリが起動し、データは作者のサーバーを通りません(サーバー自体がオンラインサービスを呼び出す場合を除きます)。
GitHubリポジトリの最終更新は–、最新バージョン(1.2.2)がレジストリに届いたのは4 か月前です。
Claude Desktop、Claude Code、Cursor、VS CodeなどのMCP対応アプリで使えます。パソコンにNode.jsも必要です。
同じカテゴリには:Context7, Codebase Memory, Exa
同じカテゴリで、ほかの作者が作ったサーバー。スターの多い順。
カードの見方:★ GitHubスターi3つの柱:レビュー · チェック · 利用状況i更新=最新バージョンをレジストリに公開した日i
Up-to-date docs for AI agents
Codebase knowledge graph for AI agents — 162 languages, sub-ms queries, 99% fewer tokens.
Connect AI agents to Exa for web search, content fetching, and multi-step research.
作者のリポジトリから書かれたとおりにコピーしたものです。PopNx MCPは内容を確認・推奨していません。HTML、画像、スクリプトは取り除いています。リンクはこのサイトの外へ移動します。
Current stable release: v1.2.2. See CHANGELOG.md for the release notes.
Context Fabric is configured for official inclusion in the Model Context Protocol (MCP) Registry.
io.github.VIKAS9793/context-fabricThe 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.
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.
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.
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.
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
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.
npx context-fabric init
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"] } } }
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.
spawn npx ENOENT ErrorsThis usually means npx is not in the system's inheritance path for the IDE.
node and the context-fabric binary.where node (Windows) or which node (Mac/Linux) to find your path. "command": "/Users/name/.nvm/versions/node/v22.14.0/bin/node",
"args": ["/Users/name/.nvm/versions/node/v22.14.0/bin/context-fabric"]
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.
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.
If something breaks, please run:
npx context-fabric doctor
npx context-fabric diag remains available as a compatibility alias.
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
| Tool | Engines | Purpose |
|---|---|---|
cf_capture | E1 | Manual context capture outside of a git commit |
cf_drift | E2 | Standalone drift check — returns severity and stale file count |
cf_query | E2 + E3 + E4 + E5 | Full context briefing for the current task |
cf_health | Local health | Report database, capture, hook, and search index health |
cf_log_decision | Storage | Persist 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
冒頭だけを表示しています。続きはリポジトリで読めます