Context7
Up-to-date docs for AI agents
作者提供的说明(英文原文)
Persistent project memory for AI coding agents: one compact digest instead of re-reading the repo.
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。
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。
选择你使用的 AI 应用,然后复制设置。
你需要
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": {
"projectmind": {
"command": "npx",
"args": [
"-y",
"@nodemint/projectmind",
"mcp"
]
}
}
}在终端中运行这条命令
claude mcp add projectmind -- npx -y @nodemint/projectmind mcp粘贴到项目中的 .cursor/mcp.json,或 ~/.cursor/mcp.json
{
"mcpServers": {
"projectmind": {
"command": "npx",
"args": [
"-y",
"@nodemint/projectmind",
"mcp"
]
}
}
}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。
粘贴到项目中的 .vscode/mcp.json
{
"servers": {
"projectmind": {
"type": "stdio",
"command": "npx",
"args": [
"-y",
"@nodemint/projectmind",
"mcp"
]
}
}
}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。
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6日
注册名称已通过 GitHub 账户 Nodemint-dev 验证,该账户拥有此仓库
我们还没有从 GitHub 读取这个仓库
我们还没有从 GitHub 读取这个仓库
它在你的电脑上运行,没有互联网端点
在你的电脑上运行,改用本地设置
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。
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。
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)。
GitHub 仓库最后更新于–,最新版本(0.6.0)于4个月前进入注册中心。
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。
同一分类中有:Context7, Codebase Memory, Exa
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
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.
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。
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
只显示了开头部分;其余内容请在仓库中阅读