
Project Lore
io.github.tamas-fm由 GitHub 帳號 tamas-fm 發布v0.1.1更新於 Oct 24, 2026
A local-first MCP server that routes development tasks to authoritative project documentation.
概覽
一個本地優先的 MCP 伺服器,為儲存庫文件建立索引,並把開發任務導向最相關、最具權威性的文件。
- 可以做什麼
- 它會掃描儲存庫中的 Markdown、文字、HTML、YAML、JSON、Mermaid 與指令檔案,依類型與權威層級分類,並建立可重建的本地 SQLite(FTS5)索引,依內容雜湊增量更新。它提供三個工具:route_task 針對指定開發任務回傳應優先閱讀的文件與章節順序;search_project_knowledge 對文件做全文檢索,並依權威層級與狀態重新排序,每筆結果顯示新鮮度;get_document_context 回傳單一文件的章節與具型別的關聯。回答是有界摘錄並附精確來源位置,而非整個檔案,同時會標記過期或被取代的來源。
- 適用情境
- 當助理需要了解專案為何如此設計、哪份文件約束某項任務,或該知識是否仍然有效時適用。它補足那些回答程式碼實作位置與相依關係的程式碼智慧伺服器。最適合擁有實質書面文件、決策紀錄或限制條件的儲存庫。
- 執行需求
- 需要 Node.js 22 或以上版本。透過 npm 套件 project-lore-mcp 以本地 stdio 程序執行,可全域安裝或以 npx 執行。需要提供要索引的儲存庫根路徑,且每個儲存庫單獨設定。若引用儲存庫以外的文件根目錄,用戶端必須被授予對這些路徑的檔案系統存取權。不需要帳號、API 金鑰或網路存取。
安裝
在 SourceWeft 中
- 開啟 儀表板中的 Project Lore,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
project-lore-mcp
A local-first MCP server that routes coding agents to the smallest useful set of project documentation — decisions, intent, and constraints — with provenance and freshness.
Code-intelligence tools (such as codebase-memory-mcp) answer where is this implemented and what depends on it. This project answers the complementary questions: why was it designed this way, which document governs this task, and is that knowledge still current?
What it does
Point it at a repository (plus optional external documentation roots). It:
- discovers Markdown, text, HTML, YAML, JSON, Mermaid, and Claude instruction files;
- classifies each document by kind and authority (instructions → canonical → design → planning → historical → generated → proposed);
- extracts headings, sections, links, frontmatter, ADR status, and explicit "read first" / "supersedes" / task-routing relationships — deterministically, with recorded provenance (structural extraction applies to Markdown; other formats receive plain-text indexing);
- stores a rebuildable local index in SQLite (FTS5), updated incrementally by content hash;
- answers task and knowledge queries with bounded excerpts and exact source locations — never whole files, flagging stale or superseded sources instead of hiding them.
MCP tools
Install and run
Requires Node.js ≥ 22.
Claude Code
Run this once from inside the repository you want to index:
For teams, commit a shared configuration instead:
The project scope uses a relative root (.) so it works on any checkout.
Claude Code will ask each user to approve project-scoped servers before
running them.
One repository, one configuration. Project Lore's command contains a fixed root path, so it always indexes one specific repository. Register it separately per repository rather than globally.
It coexists with codebase-memory-mcp — configure both; they own different questions. If your configuration references documentation outside the repository, Claude Code must be granted filesystem access to those paths (add them as additional working directories or approve the permission prompts).
Try the example
Configuration
Optional project-lore.config.yaml at the repository root:
See docs/configuration.md for the full reference and docs/concepts.md for the authority model.
Design principles
Store and retrieve documented intent conservatively. Derive structure automatically. Preserve provenance always.
- Local-only by default. No network access, no telemetry, no uploads.
The index is regenerable and safe to delete (
.project-lore/, gitignored). - Deterministic before AI. v1 uses no LLM and no embeddings. Every stored relationship records how it was extracted; inferred data can never masquerade as documented fact.
- Evidence, not instructions. Retrieved excerpts are quoted, bounded, and attributed. Document content is treated as untrusted data (see docs/provenance-and-trust.md).
Project status
v0.1.0 — first vertical slice. See docs/architecture.md, the ADRs in docs/adr/, and CONTRIBUTING.md.
License: MIT.
來源:README.md,提交 2841711
工具
0版本歷史
1- v0.1.1最新Sep 16, 2026
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