scratchbook-mcp
The scratchbook-mcp server provides a persistent, searchable codebase memory for AI coding agents. It combines hybrid semantic+BM25 code search, exact identifier and event cross-referencing, structural outlining, a work journal, and isolated deep reasoning sessions, all backed by a local LanceDB store.
Key capabilities:
Incremental codebase indexing (respecting .gitignore), with optional force re-embedding.
Hybrid code search (scratchbook_search) fusing semantic and BM25 retrieval for concept-level recall and exact identifier precision.
Exact cross-referencing: find all definitions/references for an identifier (scratchbook_refs) and map event names to send/listen sites (scratchbook_events).
Structural outlines (scratchbook_outline) with imports and top-level symbols.
Work journal (scratchbook_journal, scratchbook_recall) for writing and semantically recalling dated notes, findings, or bugs.
Gaze-and-think deep reasoning (scratchbook_gaze, scratchbook_gaze_read): dump tangled problems to a board, launch isolated AI flow sessions, produce artifacts, and persist syntheses into recall.
Status monitoring (scratchbook_status) of embedding service and indexed projects.
Forget project index (scratchbook_forget) while keeping journal notes.
Zero-dependency fallback: lexical-only mode (BM25, refs, events, outline, journal) when no embedding service is available, auto-upgrading to hybrid search when detected.
Uses an Ollama embedding endpoint (default bge-m3) to enable semantic code search, powering hybrid semantic + BM25 retrieval.
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@scratchbook-mcpsearch for code related to embedding and vector storage"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
scratchbook-mcp
A codebase-memory MCP server for AI coding agents (Claude Code and others): hybrid semantic + BM25 code search, a structural outline, a work journal, and gaze-and-think deep reasoning — over a local LanceDB.
給 AI 編碼代理(Claude Code 等)的程式碼記憶 MCP 伺服器: 混合語意 + BM25 的程式碼搜尋、結構大綱、工作日記,以及 gaze-and-think 深度推理 —— 全部建在本機 LanceDB 上。
Why / 為什麼
grep/rg only find what you can already name, and mostly within one
directory. When an agent works in a large or unfamiliar repo, it spends attention
locating code instead of understanding it. scratchbook gives the agent a
cross-directory, meaning-based index of the code, a lexical (exact-identifier)
arm fused in, a place to write notes it can recall later, and a way to think
deeply about a tangled subsystem in a clean, isolated session. It augments
grep/Read — it does not replace them.
grep/rg 只找得到你叫得出名字的東西,而且多半困在單一目錄。當代理在龐大或
陌生的 repo 裡工作,注意力都耗在「定位程式碼」而不是「理解它」。scratchbook 給代理
一份跨目錄、以意義為基礎的程式碼索引,融入一條詞彙(精確識別字)臂,一個可日後
回想的筆記空間,以及一種在乾淨隔離的 session 裡把糾結子系統想透的方法。它增強
grep/Read —— 不是取代它們。
Related MCP server: nexus-mcp-ci
Features / 特色
Hybrid retrieval / 混合檢索 — semantic (embedding) search fused with lexical BM25 via Reciprocal Rank Fusion. Meaning-based recall and exact-identifier precision (a query for
resolveAnnotatedMapsurfaces the exact symbol the embedding alone would rank below near-synonyms). Each hit is taggedsem/lex/lex+sem. 語意(向量)搜尋與詞彙 BM25 以 RRF 融合:兼得「概念召回」與「精確識別字」。每筆 結果標記由哪一臂命中(sem/lex/lex+sem)。Zero-dependency fallback / 零依賴降級 — no embedding service? Everything still works in lexical-only mode (BM25 + refs/events/outline/journal); the first index run after an embedder appears auto-upgrades to hybrid. 沒有 embedding 服務也能用:自動進入 lexical-only 模式(BM25 + 交叉引用/outline/ 日誌照常),之後偵測到端點會自動全量升級成混合檢索。
Exact cross-reference / 精確交叉引用 —
scratchbook_refsenumerates every definition and reference of an identifier: grep-shaped (LIKE prefilter + line-level regex), deliberately not BM25 — an FTS tokenizer splitssnake_caseinto an OR-flood and top-k truncation silently drops sites.scratchbook_eventscross-references string event names (.emit/.on+ wrapper methods) into an event → send sites / listen sites table and flags one-sided events — the string edges a syntactic call graph can't see.scratchbook_refs窮舉一個識別字的所有定義與引用:grep 形而非排序形(LIKE 預過濾 + 行級 regex),刻意不走 BM25 —— FTS tokenizer 會把snake_case切成 OR 洪水,top-k 截斷即靜默漏報。scratchbook_events交叉比對字串事件名 (.emit/.on與包裝方法),產出「事件 → 發送端/監聽端站點」對照表並標記單邊 事件 —— 這些字串邊是語法呼叫圖看不見的架構。Structural outline / 結構大綱 — each file's imports + top-level symbols with line numbers, to grasp organisation fast. 每個檔案的 imports + 頂層符號 + 行號,快速掌握專案組織。
Work journal + recall / 工作日記與回想 — write dated notes (decisions, findings, suspected bugs); recall them later by meaning, with an age shown so stale notes read as old. 寫下有日期的筆記(決策、發現、可疑 bug);日後以語意回想,並顯示年齡讓過時 筆記一眼看得出來。
gaze-and-think / 凝視與思考 — dump a tangled problem into a board, then launch a clean, isolated flow session that concentrates solely on it, reasons about the relationships between the pieces, writes inspectable intermediate artifacts, and converges on a synthesis you can persist into recall. 把糾結的問題丟進一個 board,再啟動一個乾淨隔離的心流 session,專注思考各片段間 的關係、產出可檢查的中間產物、收斂成綜合結論,並可持久化進 recall。
Control-char-safe snippets / 控制字元安全的片段 — invisible bytes (NUL, …) are rendered
cat -A-style (^@) so they can't be misread as spaces. 隱形位元組(NUL 等)以cat -A風格顯示(^@),避免被誤讀成空格。
Architecture / 架構
AI agent ──stdio (JSON-RPC)──▶ src/server.mjs (MCP, 11 tools)
│
┌───────────────┬──────────────┼──────────────┬───────────────┬──────────────┐
▼ ▼ ▼ ▼ ▼ ▼
indexer.mjs store.mjs embed.mjs gaze.mjs walk.mjs refs.mjs
walk→chunk LanceDB Ollama-style clean `claude git ls-files / exact xref:
→embed→upsert 3 tables + /api/embed -p` flow gitignore-aware LIKE + line
(incremental) FTS (BM25) (bge-m3, 1024) session chunk.mjs regex; event
~/.scratchbook (symbol-aware) send/listenRuntime / 執行環境: Node ESM, no build step (
node src/server.mjs).Storage / 儲存: one LanceDB at
~/.scratchbook/lancedb, every row namespaced by the canonical (realpath) project root — one server serves every project. 單一 LanceDB,每列以正規化的專案根路徑命名空間 —— 一個伺服器服務所有專案。Tables / 資料表:
chunks(symbol-aware code chunks + vectors + a BM25 FTS index),files(per-file imports + outline),journal(dated notes + vectors + FTS).Enumeration / 列舉檔案:
git ls-filesin a git repo (perfect.gitignorerespect); a manual walk with built-in ignores otherwise. Binaries, lockfiles, minified, and >512 KB files are skipped.Incremental / 增量: unchanged files skipped by
(mtime, size)then content hash; only changed files are re-read and re-embedded.
Requirements / 需求
Node ≥ 20 — that's all. Everything else is optional. 只需要 Node ≥ 20,其他都是可選的。
Quick start / 一鍵安裝
One command — no clone, no embedding service, works immediately: 一行指令 —— 不用 clone、不用 embedding 服務,立刻可用:
claude mcp add scratchbook --scope user -- npx -y github:jinyaolin/scratchbookOut of the box scratchbook runs in lexical-only mode: full-text (BM25)
search, exact cross-reference (refs / events), outline, and the journal all
work with zero external services. Semantic search switches on automatically once
an embedder is reachable (next section).
開箱即是 lexical-only 模式:BM25 全文檢索、精確交叉引用(refs / events)、
outline、日誌全部可用,零外部服務。偵測到 embedding 端點後,語意搜尋自動開啟(見下節)。
Remove with claude mcp remove scratchbook -s user.
Optional: semantic search / 可選:語意搜尋
Point scratchbook at any Ollama-style embedding endpoint — default is a local
Ollama with bge-m3 (1024-dim):
給 scratchbook 一個 Ollama 風格的 embedding 端點即可 —— 預設是本機 Ollama 的
bge-m3(1024 維):
# https://ollama.com
ollama pull bge-m3
# Ollama serves the embedding API at http://localhost:11434/api/embedThe next scratchbook_index detects the embedder and auto-upgrades the
project (full re-embed); hybrid semantic+BM25 search takes over from there.
Use SCRATCHBOOK_EMBED_URL / _MODEL / _DIM for a remote box or another model.
下一次 scratchbook_index 偵測到端點就會自動升級(全量重嵌入),之後就是語意+BM25
混合搜尋。遠端機器或其他模型用 SCRATCHBOOK_EMBED_URL / _MODEL / _DIM 指定。
Install from a clone / 從 clone 安裝
git clone https://github.com/jinyaolin/scratchbook.git
cd scratchbook
npm install
claude mcp add scratchbook --scope user \
-- node /absolute/path/to/scratchbook/src/server.mjsAny MCP client works — the server speaks stdio JSON-RPC. See your client's docs for how to register a local stdio MCP server. 任何 MCP 客戶端都行 —— 伺服器走 stdio JSON-RPC。註冊方式見你客戶端的文件。
Tools / 工具
Tool | Purpose / 用途 |
| Index/refresh a codebase (incremental). Run once per project, again after big edits. / 索引/刷新一個 codebase(增量)。 |
| Hybrid (semantic + BM25) code search → |
| Exact, exhaustive who-uses-X: every definition + reference line of one identifier (case-sensitive). / 精確窮舉某識別字的所有定義與引用行。 |
| String event-name cross-reference: event → send / listen sites, one-sided events flagged ⚠. / 字串事件名交叉引用表,單邊事件標 ⚠。 |
| Structural map: each file's imports + top-level symbols with line numbers. / 結構大綱。 |
| Write a dated work-note (decisions, findings, suspected bugs). / 寫工作日記。 |
| Hybrid search over past notes, with note age shown. / 回想筆記(顯示年齡)。 |
| Embedding health + indexed projects with counts. / 健康檢查與已索引專案。 |
| Drop a project's code index (journal kept). / 移除某專案的程式碼索引(日記保留)。 |
| Write to a board and/or launch a flow session over it. / 寫入 board 並/或啟動心流 session。 |
| Read a board (notebook, sessions, artifacts); |
Every tool takes an absolute project/path; it defaults to the server's cwd.
每個工具都可傳絕對 project/path,預設為伺服器的 cwd。
gaze-and-think / 凝視與思考
For a problem too tangled to hold in the working session, dump the pieces into a
board (a notebook), then launch a flow session — a fresh, isolated
claude -p that carries no other context and concentrates only on that board.
It reasons about the relationships between the pieces, writes intermediate
artifacts under the board's artifacts/, and converges on a synthesis. Runs in
the background; poll with scratchbook_gaze_read.
當問題糾結到裝不進當下的 session,把片段丟進一個 board(筆記本),再啟動一個
心流 session —— 一個乾淨隔離、不帶其他上下文、只專注在該 board 的 claude -p。
它思考各片段的關係、把中間產物寫進 board 的 artifacts/、收斂成綜合結論。背景
執行,用 scratchbook_gaze_read 取回。
scratchbook_gaze board="X" note="a hard piece…" # accumulate material / 累積素材
scratchbook_gaze board="X" focus="how do these relate?" project="/abs/repo"
scratchbook_gaze_read board="X" # read synthesis + artifacts
scratchbook_gaze_read board="X" remember=true # persist synthesis → recallThe flow session runs with --permission-mode acceptEdits and read/write/search
tools but no shell — it can build checkable artifacts and ground claims in
real code, but never runs arbitrary commands. A remembered synthesis is stamped
with the commit it was grounded in and its age is shown on recall, because it has
no source file to re-derive from and so does not auto-update — treat it as
understanding as of that commit, and verify against current code.
心流 session 以 --permission-mode acceptEdits 執行,有讀/寫/搜尋工具但無 shell
—— 能產出可檢查的產物、對真實程式碼 grounding,但不執行任意指令。被 remember 的
綜合結論會蓋上「當時所依據的 commit」戳記,recall 時顯示年齡:它沒有源檔可重新推導、
不會自動更新,請當成「截至該 commit 的理解」,並對照現行程式碼驗證。
Requires the
claudeCLI onPATHfor the flow session. 心流 session 需要PATH上有claudeCLI。
CLI (manual use / debugging) / 命令列(手動使用 / 除錯)
node src/cli.mjs index /path/to/your/repo # index a repo
node src/cli.mjs index /path/to/your/repo --reindex # force full re-embed
node src/cli.mjs search "where sessions are cached" --project /path/to/your/repo --full
node src/cli.mjs outline --project /path/to/your/repo --path src/lib
node src/cli.mjs status
node src/cli.mjs note "flagged N+1 in loader" --tags bug,perf --project /path/to/your/repo
node src/cli.mjs recall "what was I doing here" --project /path/to/your/repo
node src/cli.mjs forget --project /path/to/your/repoConfiguration (env) / 設定(環境變數)
Env | Default | Meaning / 意義 |
|
| LanceDB directory / LanceDB 目錄 |
|
| Ollama-style embed endpoint / embed 端點 |
|
| embedding model / 模型 |
|
| vector dimension (must match model) / 向量維度(須與模型一致) |
|
| texts per embed request / 每次請求文本數 |
|
| parallel embed requests / 並行請求數 |
|
| gaze boards directory / gaze board 目錄 |
|
| skip files larger than this / 略過超過此大小的檔 |
|
| max chunk size / chunk 上限 |
|
| min before a soft cut / chunk 下限 |
|
| overlap when window-splitting / 視窗切分重疊行數 |
Notes / limits / 注意事項與限制
The embedding dimension is baked into the LanceDB schema. Changing to a model of a different dimension means a fresh DB (delete
~/.scratchbook/lancedb). embedding 維度寫死進 LanceDB schema;換到不同維度的模型需重建 DB(刪掉該目錄)。A recalled note / remembered gaze synthesis reflects the code as it was when written and does not auto-update — it's a strong prior, not a substitute for reading current code. 回想到的筆記 / 記憶的 gaze 結論反映寫入當下的程式碼、不會自動更新 —— 是強力 的前導,不是「讀現行程式碼」的替身。
Chunking is heuristic (regex anchors), not a full parser — good enough for retrieval and outlines, not a semantic analyser. 切塊是啟發式(regex 錨點),不是完整 parser —— 對檢索/大綱夠用,不是語意分析器。
Data under
~/.scratchbook/is fully rebuildable from source — safe to delete.~/.scratchbook/下的資料完全可從原始碼重建 —— 可安心刪除。
License / 授權
Apache License 2.0 — Copyright 2026 Jinyao Lin.
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