
Understand Quickly
io.github.looptech-ai由 GitHub 帳號 looptech-ai 發布v0.1.1更新於 Oct 24, 2026
Public registry of code-knowledge graphs for AI agents with schema validation and drift detection.
概覽
一個本機 MCP 伺服器,開放公開的程式碼知識圖譜登錄檔,讓助理列出儲存庫、查詢儲存庫圖譜並搜尋概念。
- 可以做什麼
- 列出登錄檔項目,可依格式、標籤或狀態篩選,並附圖譜連結與同步日期。依登錄檔 id 或 GitHub URL 查詢儲存庫對應的圖譜,回傳漂移中繼資料,找不到時提供模糊比對建議。取得並解析單一項目的圖譜 JSON。在登錄檔中搜尋概念詞,或在單一圖譜內做節點子字串比對。
- 適用情境
- 適合讓助理確認某個儲存庫是否已有程式碼知識圖譜、查看圖譜相對目前程式碼落後多少,或在公開登錄檔中查找概念。它只是輕量封裝,適合探索性查詢而非重度分析。
- 執行需求
- 以 npm 套件形式透過 stdio 在本機執行,需要 Node 20 以上版本。預設透過網路取得公開登錄檔與統計 JSON;可用環境變數 UNDERSTAND_QUICKLY_REGISTRY 與 UNDERSTAND_QUICKLY_STATS 指向本機檔案或分支。不需驗證。
安裝
在 SourceWeft 中
- 開啟 儀表板中的 Understand Quickly,將其新增到工作區。
- 為需要使用其工具的對話啟用該服務。
Desktop only,透過 STDIO。 STDIO 服務會啟動本機處理程序,因此需要 SourceWeft 桌面主機。
其他 MCP 客戶端
參照 儲存庫 中的啟動說明。
README
@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:
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)[email protected]: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
Node 20+ is required (uses the global fetch).
Run locally
For development:
For a built binary:
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):
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:
Environment variables
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.
來源:mcp/README.md,提交 f856ff2
工具
0版本歷史
1- v0.1.1最新Sep 16, 2026
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