Menyediakan registry code-knowledge graph untuk AI client guna melakukan analisis repositori, pencarian konsep, dan deteksi drift.
Juga dikenal sebagai looptech-ai/understand-quickly, @looptech-ai/understand-quickly-mcp
- Stars
- 48
- Tools
- —
- Unduhan, 30 hari
- —
- Versi terbaru
- 0.1.1
- Commit terakhir
- 17 Sep 2026
- Lisensi
- Apache-2.0
Apa itu understand-quickly MCP?
understand-quickly adalah MCP server. Menyediakan registry code-knowledge graph untuk AI client guna melakukan analisis repositori, pencarian konsep, dan deteksi drift.
Ringkasan dibuat dari README proyek
Tentang understand-quickly MCP server
Developer dapat melakukan analisis repositori kode melalui knowledge graph menggunakan MCP server ini. Server ini menghubungkan AI client seperti Claude Desktop, Cursor, atau Codex ke sebuah registry code-knowledge graph yang sudah dikalkulasi sebelumnya, memungkinkan analisis repositori yang mendalam dan pencarian konsep. Server berjalan secara lokal melalui stdio JSON-RPC dan membutuhkan Node 20 atau versi lebih tinggi untuk berfungsi.
Anda dapat berinteraksi dengan registry menggunakan empat tool spesifik: list_repos untuk melihat entri yang tersedia, find_graph_for_repo untuk menemukan graph tertentu melalui URL GitHub atau ID, get_graph untuk mengambil data JSON yang telah di-parse, dan search_concepts untuk mencari istilah tertentu. Sistem ini juga menyertakan fitur deteksi drift bawaan, yang menyediakan metadata seperti seberapa banyak commit sebuah repositori tertinggal dari sumbernya.
Implementasi ini sengaja dibuat minimalis dan menggunakan in-memory caching dengan TTL 60 detik untuk data registry dan stats. Server ini mengandalkan registry.json publik dan dapat dikonfigurasi untuk menggunakan sumber registry atau stats kustom melalui environment variables. Meskipun belum mendukung embeddings atau streaming, server ini menyediakan cara terstruktur untuk menavigasi codebase yang kompleks melalui pencocokan konsep agregat dan pencarian node berbasis graph.
Apa yang bisa Anda lakukan dengannya
- 1Mencari konsep arsitekturalMinta daftar konsep pemrograman umum dari seluruh repositori yang tersedia di dalam registry.
Ditulis berdasarkan README proyek. Setiap use case diperiksa terhadap kutipan dari README tersebut.
Cara menginstal understand-quickly MCP
Format diverifikasi 10 Nov 2026Tambahkan ke CursorTambahkan ke GooseInstal understand-quickly MCP di Claude Code
$ claude mcp add understand-quickly -- npx -y @looptech-ai/understand-quickly-mcpInstal understand-quickly MCP di Cursor
~/.cursor/mcp.json
{
"mcpServers": {
"understand-quickly": {
"command": "npx",
"args": [
"-y",
"@looptech-ai/understand-quickly-mcp"
]
}
}
}Instal understand-quickly MCP di VS Code
.vscode/mcp.json
{
"servers": {
"understand-quickly": {
"type": "stdio",
"command": "npx",
"args": [
"-y",
"@looptech-ai/understand-quickly-mcp"
]
}
}
}Instal understand-quickly MCP di VS Code CLI
$ code --add-mcp '{"name":"understand-quickly","type":"stdio","command":"npx","args":["-y","@looptech-ai/understand-quickly-mcp"]}'Instal understand-quickly MCP di Claude Desktop
claude_desktop_config.json
{
"mcpServers": {
"understand-quickly": {
"command": "npx",
"args": [
"-y",
"@looptech-ai/understand-quickly-mcp"
]
}
}
}Instal understand-quickly MCP di Codex
$ codex mcp add understand-quickly -- npx -y @looptech-ai/understand-quickly-mcpInstal understand-quickly MCP di Codex config.toml
~/.codex/config.toml
[mcp_servers.understand-quickly] command = "npx" args = ["-y", "@looptech-ai/understand-quickly-mcp"]
Instal understand-quickly MCP di Gemini CLI
$ gemini mcp add -s user understand-quickly npx -- -y @looptech-ai/understand-quickly-mcpInstal understand-quickly MCP di Gemini settings.json
~/.gemini/settings.json
{
"mcpServers": {
"understand-quickly": {
"command": "npx",
"args": [
"-y",
"@looptech-ai/understand-quickly-mcp"
]
}
}
}Konfigurasi
UNDERSTAND_QUICKLY_REGISTRY
| Nama | Deskripsi | Tipe |
|---|---|---|
| UNDERSTAND_QUICKLY_REGISTRY | — |
Pengecekan otomatis
Berjalan 13 Nov 2026Laporan lengkap →Lolos checks otomatis kami. Ini bukan audit keamanan: baca kodenya dan berikan server akses paling terbatas yang dibutuhkannya. Cara kerja checks
- Source repository terhubung dan dapat dijangkauLolos
- Lisensi open source (OSI)Lolos
- Ada commit atau release dalam satu tahun terakhirLolos
- Nama package tidak mirip dengan package populerLolos
- Remote endpoint menggunakan HTTPSTidak diperiksa
- Tools yang bersifat destruktif telah diberi labelTidak diperiksa
- Tidak ada instruksi tersembunyi untuk modelLolos
- Tidak ada advisory kritis pada paket atau dependensinyaLolos
README
Lihat di GitHub@looptech-ai/understand-quickly-mcp
A thin Model Context Protocol server that exposes the understand-quickly registry to any MCP client (Claude Desktop, Codex, Cursor, etc.).
Status: stub-quality. It works end-to-end but is intentionally minimal — no streaming, no embeddings, no auth.
What it does
It wraps the public registry.json and exposes four tools:
| Tool | Params | Returns |
|---|---|---|
list_repos |
{ format?, tag?, status? } |
Array of { id, format, description, status, tags, last_synced, graph_url } |
find_graph_for_repo |
{ id?, github_url? } (at least one required) |
Single registry entry's graph_url + drift metadata, or { found: false, suggestions: [...] } with up to 5 fuzzy-matched ids |
get_graph |
{ id } |
Parsed graph JSON for that entry's graph_url |
search_concepts |
{ query, id? } |
Default: aggregated concept matches from the precomputed stats.json (single GET, cached 60s). With id: substring match across one graph's nodes. Falls back to a capped cross-graph fan-out if stats.json is unreachable. |
The registry response is cached in-memory for 60 seconds. stats.json uses an
identical 60-second TTL cache.
find_graph_for_repo
Accepts either an id (the registry id, owner/repo) or a github_url. The
URL parser tolerates:
https://github.com/owner/repohttps://github.com/owner/repo.githttps://github.com/owner/repo/(trailing slash)https://github.com/owner/repo/tree/main/...(branch / sub-path)git@github.com:owner/repo.git
When the entry is found, the response includes last_synced, last_sha,
source_sha, head_sha, commits_behind, and a pretty drift_summary
(e.g. "behind by 17 commits") when those fields are present in the registry.
If the entry is not found, the response is
{ found: false, suggestions: [...] } with up to 5 fuzzy-matched ids
(Levenshtein distance ≤ 3 against the lowercased id).
search_concepts
By default — that is, when id is not provided — search_concepts reads the
precomputed stats.json aggregate (a single, cached GET) and returns matching
concept terms with their entry counts and up to 3 sample registry ids. This
replaces the previous behaviour, which fanned out up to 5 graph fetches at
request time.
When id is provided, it falls back to the legacy single-graph node search
(substring match against id / label / name). When stats.json is
unavailable (404 or schema mismatch), it falls back to the capped cross-graph
fan-out for backward compatibility.
The source field on the response indicates which mode served the request:
"stats", "graph", or "fanout".
Install
cd mcp
npm install
npm run build # compiles TypeScript -> dist/
npm test # runs node:test across registry/cache and tool logic
Node 20+ is required (uses the global fetch).
Run locally
For development:
npm run dev
For a built binary:
npm start
The server speaks stdio JSON-RPC. It will not respond to keystrokes — point an MCP client at it.
Register with Claude Desktop
Add the following to Claude Desktop's claude_desktop_config.json (the path is
~/Library/Application Support/Claude/claude_desktop_config.json on macOS):
{
"mcpServers": {
"understand-quickly": {
"command": "npx",
"args": [
"tsx",
"/absolute/path/to/understand-quickly/mcp/src/index.ts"
],
"env": {
"UNDERSTAND_QUICKLY_REGISTRY": "https://looptech-ai.github.io/understand-quickly/registry.json"
}
}
}
}
Replace /absolute/path/to/... with the actual path to your checkout. Restart
Claude Desktop after saving.
If you would rather run the compiled output, swap to:
{
"mcpServers": {
"understand-quickly": {
"command": "node",
"args": ["/absolute/path/to/understand-quickly/mcp/dist/index.js"]
}
}
}
Environment variables
| Variable | Default | Purpose |
|---|---|---|
UNDERSTAND_QUICKLY_REGISTRY |
https://looptech-ai.github.io/understand-quickly/registry.json |
Override the registry source (e.g. point at a local file or a fork). |
UNDERSTAND_QUICKLY_STATS |
https://looptech-ai.github.io/understand-quickly/stats.json |
Override the precomputed stats source consumed by search_concepts. |
Current limitations
- In-memory cache only. Every server process refetches once a minute. No cross-process or on-disk cache.
- Cross-graph fan-out is only a fallback. When
search_conceptsfalls back (no stats.json), it scans only the first 5status: okentries sequentially. - Substring search is dumb. No fuzzy matching, no ranking, no embeddings.
- No streaming or progress reporting. Tools block until the upstream responds.
- Best-effort node enumeration. The single-graph fallback assumes the graph
has a
nodes/entities/concepts/itemsarray; otherwise it walks top-level array values. - No retries or backoff on upstream
graph_urlfetch failures — failed fetches return an empty result for that entry instead of erroring out.
These are all acceptable for an MVP. If you need more, open an issue.
Pertanyaan tentang understand-quickly MCP
Bagaimana cara menambahkan understand-quickly MCP ke Claude Code?
Jalankan claude mcp add understand-quickly -- npx -y @looptech-ai/understand-quickly-mcp di terminal Anda. Tambahkan --scope user agar tersedia di semua proyek.
Bagaimana cara menambahkan understand-quickly MCP ke Cursor?
Buka ~/.cursor/mcp.json dan tambahkan server di bawah mcpServers, menggunakan config Cursor yang ditunjukkan di bagian instalasi di atas.
Tool apa saja yang disediakan oleh server ini?
Server ini menyediakan empat tool: list_repos, find_graph_for_repo, get_graph, dan search_concepts.
Apakah server ini mendukung autentikasi?
Tidak, versi saat ini adalah implementasi minimal tanpa autentikasi.
Bagaimana cara kerja tool search_concepts?
Tool ini utamanya menggunakan file stats.json yang sudah dikalkulasi sebelumnya untuk pencocokan konsep agregat, dan akan beralih ke pemindaian lintas-graph jika file stats tidak tersedia.