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.

已驗證STDIO僅桌面知識與記憶開發者工具

概覽

AI 產生的概覽

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。
安裝前請注意
操作為唯讀查詢,但會存取遠端登錄檔與圖譜位址,請求會送往第三方,且這兩個環境變數可改變請求目標。結果依行程記憶體快取 60 秒,圖譜取得失敗時會默默回傳空結果而非報錯。README 自稱此伺服器為 stub 品質,沒有驗證、串流輸出或排序。

安裝

在 SourceWeft 中

  1. 開啟 儀表板中的 Understand Quickly,將其新增到工作區。
  2. 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:

ToolParamsReturns
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/repo
  • https://github.com/owner/repo.git
  • https://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

bash
cd mcpnpm installnpm 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:

bash
npm run dev

For a built binary:

bash
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):

json
{  "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:

json
{  "mcpServers": {    "understand-quickly": {      "command": "node",      "args": ["/absolute/path/to/understand-quickly/mcp/dist/index.js"]    }  }}

Environment variables

VariableDefaultPurpose
UNDERSTAND_QUICKLY_REGISTRYhttps://looptech-ai.github.io/understand-quickly/registry.jsonOverride the registry source (e.g. point at a local file or a fork).
UNDERSTAND_QUICKLY_STATShttps://looptech-ai.github.io/understand-quickly/stats.jsonOverride 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_concepts falls back (no stats.json), it scans only the first 5 status: ok entries 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 / items array; otherwise it walks top-level array values.
  • No retries or backoff on upstream graph_url fetch 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
SourceWeft 尚未在沙箱中啟動過該軟體包,因此其工具未知。

版本歷史

1
  1. v0.1.1最新Sep 16, 2026

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