Context7
Up-to-date docs for AI agents
Beschreibung vom Entwickler (englisches Original)
Persistent project memory for AI coding agents: one compact digest instead of re-reading the repo.
Diese drei werden nie zu einer Punktzahl zusammengefasst: Meinungen, Messungen und Beliebtheit sind verschiedene Dinge.
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Öffne Claude Desktop → Settings → Developer → Edit Config und füge dies in claude_desktop_config.json ein
{
"mcpServers": {
"projectmind": {
"command": "npx",
"args": [
"-y",
"@nodemint/projectmind",
"mcp"
]
}
}
}Führe das in deinem Terminal aus
claude mcp add projectmind -- npx -y @nodemint/projectmind mcpIn .cursor/mcp.json in deinem Projekt oder in ~/.cursor/mcp.json einfügen
{
"mcpServers": {
"projectmind": {
"command": "npx",
"args": [
"-y",
"@nodemint/projectmind",
"mcp"
]
}
}
}Dein Browser fragt, ob er die App öffnen darf, dann zeigt die App die Einstellungen und bittet dich um Bestätigung. Schlüsselwerte bleiben leer, damit du sie selbst einträgst.
In .vscode/mcp.json in deinem Projekt einfügen
{
"servers": {
"projectmind": {
"type": "stdio",
"command": "npx",
"args": [
"-y",
"@nodemint/projectmind",
"mcp"
]
}
}
}Dein Browser fragt, ob er die App öffnen darf, dann zeigt die App die Einstellungen und bittet dich um Bestätigung. Schlüsselwerte bleiben leer, damit du sie selbst einträgst.
Dieser Server läuft auf deinem Computer, und ChatGPT kann sich nur mit Servern im Internet verbinden. Wähle eine andere App.
Deine Auswahl bleibt in einem Cookie auf diesem Gerät; kein Konto nötig.
Diese Einstellungen werden aus dem Registry-Eintrag des Entwicklers erzeugt. Wenn in der README etwas anderes steht, folge der README.
Läuft auf deinem Computer; die Tool-Liste erscheint nach der Installation in deiner KI-App.
Wir prüfen Informationen, die jeder sehen kann; den Code haben wir nicht gelesen. Checks, die nicht zutreffen, werden nicht mitgezählt. · Geprüft am 6. Nov. 2026
Der Registry-Name wurde mit dem GitHub-Konto Nodemint-dev bestätigt, dem das Repo gehört
Dieses Repo haben wir noch nicht von GitHub gelesen
Dieses Repo haben wir noch nicht von GitHub gelesen
Dieser Server läuft auf deinem Computer; es gibt keinen Endpunkt im Internet
Läuft auf deinem Computer und nutzt stattdessen lokale Einstellungen
Läuft auf deinem Computer; Tool-Listen können wir nur von Endpunkten im Internet lesen
Verglichen mit allen aktiven Servern dieser Kategorie in der Registry. Sterne zählen nur, wenn dem Herausgeber das Repo gehört.
Er hat 1 von 1 zutreffenden Basischecks bestanden. Den Code haben wir nicht gelesen. Bevor du einen Schlüssel oder Token hinzufügst, sieh nach, welche Zugriffe der Server verlangt, und nutze einen Schlüssel, der nur die Berechtigungen hat, die du brauchst.
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Das GitHub-Repo wurde zuletzt – geändert, und die neueste Version (0.6.0) kam vor 4 Monaten in die Registry.
Apps, die MCP unterstützen, etwa Claude Desktop, Claude Code, Cursor und VS Code. Außerdem brauchst du Node.js auf deinem Computer.
In derselben Kategorie: Context7, Codebase Memory, Exa
Server derselben Kategorie von anderen Entwicklern, sortiert nach Sternen.
So liest du eine Karte:★ GitHub-SterneiDrei Säulen: Bewertungen · Checks · NutzungiAktualisiert = neueste Version in der Registry veröffentlichti
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.
Unverändert aus dem Repo des Entwicklers übernommen. PopNx MCP hat den Inhalt weder geprüft noch empfiehlt es ihn. HTML, Bilder und Skripte wurden entfernt. Links führen von dieser Website weg.
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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