Context7
Up-to-date docs for AI agents
제작자가 쓴 설명(영어 원문)
Persistent project memory for AI coding agents: one compact digest instead of re-reading the repo.
이 세 가지는 절대 하나의 점수로 합치지 않아요. 의견, 측정, 인기는 서로 다른 것이니까요.
이 서버는 아직 쉬운 말로 된 요약을 쓰지 못했어요. 아래는 레지스트리에서 읽어 온 사실이에요.
쓰고 있는 AI 앱을 고른 다음 설정을 복사하세요.
필요한 것
어떤 앱을 쓰세요?
위에서 앱을 고르세요. 기억해 두었다가 다음부터는 버튼에 “…에 설치”라고 표시해요.
Claude Desktop → Settings → Developer → Edit Config를 열고 claude_desktop_config.json에 이 내용을 붙여 넣으세요
{
"mcpServers": {
"projectmind": {
"command": "npx",
"args": [
"-y",
"@nodemint/projectmind",
"mcp"
]
}
}
}터미널에서 이 명령어를 실행하세요
claude mcp add projectmind -- npx -y @nodemint/projectmind mcp프로젝트의 .cursor/mcp.json 또는 ~/.cursor/mcp.json에 붙여 넣으세요
{
"mcpServers": {
"projectmind": {
"command": "npx",
"args": [
"-y",
"@nodemint/projectmind",
"mcp"
]
}
}
}브라우저가 앱을 열지 물어본 다음, 앱이 설정을 보여 주고 확인을 요청해요. 키 값은 비워 두니 직접 입력하세요.
프로젝트의 .vscode/mcp.json에 붙여 넣으세요
{
"servers": {
"projectmind": {
"type": "stdio",
"command": "npx",
"args": [
"-y",
"@nodemint/projectmind",
"mcp"
]
}
}
}브라우저가 앱을 열지 물어본 다음, 앱이 설정을 보여 주고 확인을 요청해요. 키 값은 비워 두니 직접 입력하세요.
이 서버는 내 컴퓨터에서 실행되는데, ChatGPT는 인터넷에 있는 서버에만 연결할 수 있어요. 다른 앱을 고르세요.
선택한 앱은 이 기기의 쿠키에만 저장돼요. 계정은 필요 없어요.
이 설정은 제작자의 레지스트리 정보로 만들었어요. README와 다르면 README를 따르세요.
내 컴퓨터에서 실행돼요. 설치하면 AI 앱에 도구 목록이 나타나요.
누구나 볼 수 있는 정보를 점검했고, 코드는 읽지 않았어요. 해당하지 않는 항목은 개수에서 뺐어요. · 2026년 11월 6일 점검
레지스트리 이름이 GitHub 계정 Nodemint-dev 기준으로 확인되었고, 이 계정이 저장소를 소유하고 있어요
아직 GitHub에서 이 저장소를 읽어 오지 않았어요
아직 GitHub에서 이 저장소를 읽어 오지 않았어요
내 컴퓨터에서 실행되는 서버라 인터넷 엔드포인트가 없어요
내 컴퓨터에서 실행되며, 대신 로컬 설정을 사용해요
내 컴퓨터에서 실행돼요. 도구 목록은 인터넷 엔드포인트가 있는 서버에서만 읽을 수 있어요
레지스트리에서 이 카테고리의 활성 서버 전체와 비교했어요. 스타는 게시자가 저장소를 소유한 경우에만 세요.
해당하는 기본 점검 1개 중 1개를 통과했어요. 코드는 읽지 않았어요. 키나 토큰을 넣기 전에 서버가 어떤 권한을 요구하는지 확인하고, 꼭 필요한 권한만 있는 키를 쓰세요.
레지스트리에 필수 키가 나와 있지 않아요. 다만 처음 쓸 때 로그인을 요구하는 서버도 있어요.
내 컴퓨터에서 실행돼요. AI 앱이 필요할 때 실행하고, 데이터는 제작자의 서버를 거치지 않아요(서버 자체가 온라인 서비스를 호출하는 경우는 제외).
GitHub 저장소는 마지막으로 – 변경됐고, 최신 버전(0.6.0)은 4개월 전 레지스트리에 올라왔어요.
Claude Desktop, Claude Code, Cursor, VS Code처럼 MCP를 지원하는 앱에서 쓸 수 있어요. 내 컴퓨터에 Node.js도 있어야 해요.
같은 카테고리에는: Context7, Codebase Memory, Exa
같은 카테고리에서 다른 제작자가 만든 서버예요. 스타 순으로 정렬했어요.
카드 보는 법:★ GitHub 스타i세 기둥: 리뷰 · 점검 · 사용 정도i업데이트 = 최신 버전을 레지스트리에 공개한 날i
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.
제작자의 저장소에서 쓰인 그대로 가져왔어요. PopNx MCP가 검토하거나 보증한 내용이 아니에요. HTML, 이미지, 스크립트는 제거했어요. 링크를 누르면 이 사이트를 벗어나요.
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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