Context7
Up-to-date docs for AI agents
作者による説明(英語の原文)
Persistent project memory for AI coding agents: one compact digest instead of re-reading the repo.
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": {
"projectmind": {
"command": "npx",
"args": [
"-y",
"@nodemint/projectmind",
"mcp"
]
}
}
}ターミナルでこれを実行します
claude mcp add projectmind -- npx -y @nodemint/projectmind mcpプロジェクトの .cursor/mcp.json、または ~/.cursor/mcp.json に貼り付けます
{
"mcpServers": {
"projectmind": {
"command": "npx",
"args": [
"-y",
"@nodemint/projectmind",
"mcp"
]
}
}
}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": {
"projectmind": {
"type": "stdio",
"command": "npx",
"args": [
"-y",
"@nodemint/projectmind",
"mcp"
]
}
}
}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6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 Nodemint-dev 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(0.6.0)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。
Persistent, compact project memory for AI coding agents. Your agent reads one small digest instead of re-scanning the codebase every session — and a local ledger shows you exactly how many tokens (and dollars) that saved.
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Every AI coding session starts the same way: the agent has no memory of your project, so it re-reads files, re-derives your architecture, or asks you to re-explain decisions you made months ago. You pay for that in tokens, time, and wrong guesses — every single session, on every machine, for every teammate.
projectmind fixes this with a structured, persistent project map (.projectmind/map.json): your modules, their dependencies, your architectural decisions, your conventions, your domain glossary. The agent reads a ~400-token digest first, drills into single nodes only when needed, and writes back what it learns — so the *next* session (yours, a teammate's, or a different AI tool's) starts already knowing the project.
The digest scales with your project's *conceptual* size (modules, decisions), not its *byte* size — so the savings grow with the repo.
npm install -g @nodemint/projectmind
cd your-repo projectmind init
That single init does everything: scaffolds the map, seeds it from your repo layout, detects which AI agents are installed on your machine and wires each one (MCP server + a rules file carrying the live map), adds .projectmind/ to your .gitignore (the map stays local — nothing to push), and installs the git hook that keeps the map fresh on every commit.
Then the one step no tool can do for you: restart your agent (or start a new chat session). AI agents load their config and rules files at session start — a chat that was already open keeps its old context, and every session after that has the map automatically. Using the VS Code savings extension? Cmd+Shift+P → *"Developer: Reload Window"* once.
Want it available in every future project without even running `init`?
projectmind setup --global # registers the MCP server once, user-wide
(projectmind init --bare scaffolds only, and projectmind setup re-wires agents on demand, if you prefer the pieces separately.)
The MCP server keeps a local savings ledger: every time your agent reads the map instead of scanning files, it records the tokens actually served vs. the estimated tokens of the files the agent would have read instead.
Check it anytime:
$ projectmind savings
projectmind savings (estimated — local ledger, never leaves this machine)
Total saved: ~20.5k tokens across 22 map read(s) ≈ $0.0614 at sonnet-tier input pricing ($3/MTok, as of 2026-06) Today: ~20.5k tokens (22 read(s))
By tool: mind_digest 12 reads ~18.5k saved mind_query 6 reads ~1210 saved mind_context 4 reads ~712 saved
Methodology: tokens ≈ ceil(bytes/4); baseline = files the agent would have read instead.
The dollar figure needs zero configuration: it defaults to Sonnet-tier input pricing from a built-in table of published rates (Haiku $1 / Sonnet $3 / Opus $5 / Fable $10 per MTok input, as of June 2026), and says exactly which assumption it used. If you run a different model, set savings.model (or an exact savings.inputPricePerMTok) in .projectmind/config.json — but nobody has to.
*(Real output from a scripted day of agent work on the sample project in test/fixtures/ — 12 session starts, 6 module drill-ins, 4 task-context reads. Reproduce the per-session number with npm run benchmark.)*
VS Code status bar: the integrations/vscode extension shows a live ✦ ~20.5k tokens saved counter that reads the same local ledger. Zero dependencies, zero network.
The ledger is honest by design: every number is labelled an estimate (ceil(bytes ÷ 4), the rough English+code average), savings are floored at zero, and the file is gitignored — it's your private data, on your machine, deletable at any time.
One deliberate gap, stated plainly: the ledger only counts *tool calls* (mind_digest, mind_query, mind_context). The embedded digest described below never requires a tool call — that's the whole point — so its savings don't show up as a number here. projectmind savings names which rules files carry it instead of inventing a token count we can't actually observe.
The thing every agent session loses is *working state*: what you were in the middle of, what's next, the gotcha you just discovered. Code graphs can't capture it; chat history dies with the session. projectmind carries it over:
$ projectmind digest
Order-management HTTP API with JWT auth, backed by PostgreSQL. Stack: node, express, postgres
Adding refund support to orders-route; next: write the refund tests
Your agent calls mind_handoff before the session ends (or before its context gets compacted); the note leads the very next digest, so the next session — tonight on your laptop, tomorrow on your desktop, or a different AI tool entirely — resumes in one read instead of re-deriving the task. Notes live in your gitignored local overlay: personal working state, never committed, cleared with mind_handoff({clear: true}) when done. Humans can use it too: projectmind handoff "note".
No language assumptions, no platform assumptions: the full test suite (85 tests, including the git-hook end-to-end and offline-guarantee tests) runs in CI on Linux, macOS, and Windows × Node 18/20/22. Paths, globs, atomic renames, and the installed git hook are exercised on all three. The map format is plain JSON — nothing OS-specific is ever written to your repo.
Verified against real open-source repos (fresh clone → projectmind init --seed, 2026-07-02):
| Repo | Stack detected | Seeded nodes | Repo size (est.) | Digest |
|---|---|---|---|---|
| expressjs/express | node | examples, lib, test | ~175k tokens / 152 files | ~86 tokens |
| pallets/flask | python | examples, src, tests | ~153k tokens / 104 files | ~111 tokens |
| flutter/pinball | dart, flutter | lib, packages, test, web | ~513k tokens / 637 files | ~99 tokens |
That's the core scaling property in the wild: the digest tracks a project's *conceptual* size, staying ~100 tokens whether the repo is 150k or 500k tokens. (A freshly seeded digest is a starter skeleton — a curated map with decisions and conventions lands around 400 tokens, like the benchmark fixture. We quote the honest per-session savings number — 78.9% — from the benchmark, not from these whole-repo ratios.)
flowchart LR
subgraph agents["Your AI agents"] A1[Claude Code] A2[Cursor] A3[Gemini / Codex / Copilot] end subgraph pm["projectmind"] MCP["MCP server\n(stdio, 6 tools)"] CORE["core\n(atomic writes, schema-validated)"] HOOK["git post-commit hook\n+ watch mode"] end subgraph repo["Your repo"] MAP[".projectmind/map.json\nnodes · edges · decisions\nconventions · glossary"] DIG[".projectmind/digest.md\n+ embedded in CLAUDE.md etc."] end LEDGER[".projectmind/ledger.json\nlocal savings ledger (gitignored)"]
A1 <--> MCP A2 <--> MCP A3 <--> MCP MCP --> CORE HOOK --> CORE CORE <--> MAP CORE --> DIG MCP --> LEDGER
Two update paths keep the map current without burning tokens:
projectmind watch) map changed files to modules via globs and bump freshness. Pure local computation, zero LLM.mind_update — a few tokens once, instead of re-discovery every session.There is deliberately no background LLM summarization — that would burn the tokens this tool exists to save.
# shopflow-api — project map
Order-management HTTP API with JWT auth, backed by PostgreSQL. Stack: node, express, postgres
…
…
~412 tokens for the whole thing. The long fields (notes, rationale, file lists) are excluded from the digest and only surface through mind_query — cheap by default, detail on demand.
| Tool | Purpose |
|---|---|
mind_digest() | Read first every session. The compact map. |
mind_context({files}) | Task-scoped. Only the modules a task touches + their direct deps. The cheapest read when you know the files. |
mind_query(node) | Full detail on one node: files, notes, edges. |
mind_search(term) | Find nodes / decisions / glossary by keyword. |
mind_update(delta) | Record a structural change, decision, or convention. |
mind_handoff(note) | Leave a "resume here" note that leads the next session's digest. |
mind_stats() | Map size, digest cost, and your savings ledger. |
projectmind setup idempotently writes the MCP config and a workflow rules block for each agent — merging into existing configs, never clobbering (an unparseable config is backed up and skipped).
Here's the part that matters: MCP tools are a *nudge* — no server can force a model to call one, so an agent can (and sometimes will) skip mind_digest on a plain "explain this project" question, especially mid-task. But rules files (CLAUDE.md, .cursorrules, .windsurfrules, GEMINI.md, AGENTS.md, .github/copilot-instructions.md) are loaded into every agent's context unconditionally, with zero model choice involved — that's the actual mechanism, not a coincidence. So setup doesn't just write instructions to call mind_digest; it embeds the live digest itself between markers, and every subsequent mind_update, CLI edit, git-commit hook run, or watch save re-syncs it automatically. The project map is present in context from message one, on every agent, with no tool call required at all
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