原型 · 预览版:收录官方注册中心里的全部服务器,每 30 分钟同步一次。评价和账户功能将在后续阶段推出。
PopNx MCP为使用 AI 的人准备的 MCP 服务器 · 用大白话讲清楚

LoreDocs

作者:labyrinth-analytics版本 0.1.32i★ –i在你的电脑上运行
注册名称io.github.labyrinth-analytics/loredocs

安装

概览

作者提供的说明(英文原文)

Local-first knowledge vaults for project docs and specs. Tagged, versioned, full-text searchable.

PopNx MCP 的三大支柱

这三项永远不会合并成一个分数:意见、测量和人气是不同的东西。

它能帮你做什么

我们还没有为它写通俗的介绍。下面是我们从注册中心读取到的事实。

  • 连接方式: 在你的电脑上运行
  • 传输方式: stdio
  • 分类: 搜索与 AI 记忆
  • 你需要填写:LOREDOCS_PRO

安装

选择你使用的 AI 应用,然后复制设置。

安装之前

标为机密的值是你账户的钥匙。千万不要分享给别人,也不要粘贴到聊天里。

你需要

  • PyPI已安装 Python 的 uv(用于 uvx 命令)

你用的是哪个应用?

在上方选择你的应用。我们会记住它,下次按钮会显示“安装到 …”。

打开 Claude Desktop → Settings → Developer → Edit Config,把下面的内容粘贴到 claude_desktop_config.json

{
  "mcpServers": {
    "loredocs": {
      "command": "uvx",
      "args": [
        "loredocs"
      ],
      "env": {
        "LOREDOCS_PRO": "your-value"
      }
    }
  }
}

在终端中运行这条命令

claude mcp add loredocs --env LOREDOCS_PRO=your-value -- uvx loredocs

运行前请先填写空着的值。

在 Cursor 中打开

或者自己复制设置

粘贴到项目中的 .cursor/mcp.json,或 ~/.cursor/mcp.json

{
  "mcpServers": {
    "loredocs": {
      "command": "uvx",
      "args": [
        "loredocs"
      ],
      "env": {
        "LOREDOCS_PRO": "your-value"
      }
    }
  }
}

浏览器会先询问是否打开应用,然后应用会显示设置并请你确认。密钥的值会留空,由你自己填写。

在 VS Code 中打开

或者自己复制设置

粘贴到项目中的 .vscode/mcp.json

{
  "servers": {
    "loredocs": {
      "type": "stdio",
      "command": "uvx",
      "args": [
        "loredocs"
      ],
      "env": {
        "LOREDOCS_PRO": "your-value"
      }
    }
  }
}

浏览器会先询问是否打开应用,然后应用会显示设置并请你确认。密钥的值会留空,由你自己填写。

这个服务器在你的电脑上运行,而 ChatGPT 只能连接互联网上的服务器。请选择其他应用。

你的选择通过 Cookie 保存在这台设备上,无需账户。

需要你填写的值

  • LOREDOCS_PRO 机密 env
    Pro license key. Leave unset for the free tier.

这些设置是根据作者在注册中心的条目生成的。如果 README 的写法不同,请以 README 为准。

它能做什么

它在你的电脑上运行;安装后,工具列表会显示在你的 AI 应用中。

基础检查i

我们检查的是任何人都能看到的信息;我们没有阅读代码。不适用的检查项不计入总数。 · 检查于 2026年11月6日

  • ✓ 通过

    作者与仓库一致

    注册名称已通过 GitHub 账户 labyrinth-analytics 验证,该账户拥有此仓库

  • … 尚未检查

    仍在维护

    我们还没有从 GitHub 读取这个仓库

  • … 尚未检查

    有许可证

    我们还没有从 GitHub 读取这个仓库

  • – 不适用

    端点有响应

    它在你的电脑上运行,没有互联网端点

  • – 不适用

    使用 OAuth 登录

    在你的电脑上运行,改用本地设置

  • … 尚未检查

    工具描述中没有隐藏指令

    它在你的电脑上运行;我们只能读取互联网端点的工具列表

与“搜索与 AI 记忆”中的其他服务器比较i

  • 星标数多于此分类中其他 7247 个服务器的 0%
  • 发布到注册中心的时间比此分类中其他 7247 个服务器的 81% 更新
  • 基础检查结果等于或优于此分类中其他 7247 个服务器的 100%

与注册中心里这个分类中所有活跃的服务器比较。只有当发布者拥有该仓库时,星标数才计入。

常见问题

LoreDocs 安全吗?

它在 1 项适用的基础检查中通过了 1 项。我们没有阅读代码。在添加密钥或令牌之前,请先看看这个服务器要求哪些权限,并使用只具备所需权限的密钥。

它需要哪些密钥或代码?

你需要填写:LOREDOCS_PRO

它在我的电脑上运行还是在云端?

它在你的电脑上运行。你的 AI 应用会在需要时启动它,数据不会经过作者的服务器(除非它自己调用在线服务)。

它还在维护吗?

GitHub 仓库最后更新于–,最新版本(0.1.32)于8天前进入注册中心。

哪些应用可以使用它?

支持 MCP 的应用,例如 Claude Desktop、Claude Code、Cursor 和 VS Code。你的电脑上还需要有 Python (uv)。

有什么可以替代它?

同一分类中有:Context7, Codebase Memory, Exa

替代选择

同一分类中其他作者的服务器,按星标数排序。

如何看卡片:★ GitHub 星标i三大支柱:评价 · 检查 · 使用情况i更新 = 最新版本发布到注册中心的时间i

Context7

作者:upstash

Up-to-date docs for AI agents

电脑或云端★ 64,224MIT今天更新
安装

Codebase Memory

作者:DeusData

Codebase knowledge graph for AI agents — 162 languages, sub-ms queries, 99% fewer tokens.

在你的电脑上运行★ 48,148MIT20天前更新
安装

Exa

作者:exa-labs

Connect AI agents to Exa for web search, content fetching, and multi-step research.

在云端运行★ 5,213MIT上个月更新
安装

作者的 README

打开 README

按原文从作者的仓库复制。PopNx MCP 没有审查或背书其内容。HTML、图片和脚本已被移除。链接会离开本站。

LoreDocs v0.1.32

Your AI project's knowledge base. Organized, searchable, version-tracked.

LoreDocs gives Claude persistent access to your project documentation -- specs, guides, architecture decisions, reference docs -- so it never loses context between sessions. Works with Claude Code, Cowork, Cursor, OpenAI Codex, and Hermes Agent.

Install directly from Claude Code's plugin marketplace, or via PyPI: uvx loredocs

Quick Start

Prerequisites: uv (fast Python package manager).

# Install uv: https://docs.astral.sh/uv/getting-started/installation/

Clone and install

cd /path/to/loredocs uv sync

For detailed installation instructions, see INSTALL.md.

Using the Claude Agent SDK directly? git clone the public repo and point the SDK's local-directory plugin loader at it -- the repo root is a self-contained plugin directory (.claude-plugin/plugin.json + .mcp.json). No separate SDK-installable bundle exists or is needed.

Using LoreDocs

Claude Code (Terminal)
claude --plugin-dir /path/to/loredocs

Or inside an existing session:

/plugin add /path/to/loredocs

Once loaded, Claude has access to all 49 LoreDocs MCP tools automatically. Ask Claude to "create a vault for this project" or "find the architecture doc" and it uses the tools on its own.

Cowork (Desktop App)
  1. Click + next to the prompt box
  2. Select Plugins > Add plugin
  3. Browse to the loredocs source folder

Shared Database Access: Cowork runs in a sandboxed VM. To access docs saved from Claude Code, ask Claude:

"Mount my ~/.loredocs folder"

Hermes Agent

For Hermes Agent users, add LoreDocs as an MCP server under mcp_servers: in ~/.hermes/config.yaml (see INSTALL.md for the full entry), then restart Hermes.

All 49 LoreDocs MCP tools are then available in your Hermes sessions.

Hermes Disclosures

When using LoreDocs with Hermes Agent:

  • Installation: First run downloads the loredocs version you pin from PyPI via uvx. There is no automatic self-update.
  • Data Storage: All vault data is stored locally at ~/.loredocs/ in SQLite format. No data is sent to cloud services or external servers.
  • Pro License Validation: Pro tier activation uses offline Ed25519 cryptography to validate your license key. No network request is made during validation.
  • Telemetry: LoreDocs does not collect or transmit telemetry or usage data.
  • Network calls: Pro semantic search downloads the BAAI/bge-small-en-v1.5 embedding model from Hugging Face once, on first use. The optional Notion import calls the Notion API with a token you provide. LoreDocs makes no other network calls.

How It Works

LoreDocs organizes knowledge into vaults -- named containers for related documents. Each vault can hold specs, guides, decisions, checklists, or any text you want Claude to remember.

~/.loredocs/loredocs.db          <-- SQLite database (metadata, search index)

~/.loredocs/vaults/ / <-- Document files on disk

Key concepts:

  • Vaults group related docs by project or topic
  • Documents are text files with metadata (tags, categories, priority, notes)
  • Version history tracks every change to every document
  • Full-text search via SQLite FTS5 finds anything instantly
  • Injection loads vault content into Claude's context on demand

Your Data is Always Available

LoreDocs works through MCP tools when they are available and falls back to bundled scripts automatically when they are not. Your vault documents are safe regardless of MCP status -- the same add, search, and retrieve operations work either way. You do not need to configure anything; the plugin skill handles the switch silently.

Verify Installation

After installing, verify LoreDocs is working by asking Claude:

"Run vault_list and show me the results."

If you see a list of vaults (or an empty list if this is your first time), LoreDocs is connected. If you get an error about missing tools, re-run uv sync and reload the plugin.

Recommended CLAUDE.md Setup

For the best experience, add the following snippet to your ~/.claude/CLAUDE.md (global) or your project's CLAUDE.md. This tells Claude how to use LoreDocs consistently across sessions.

## LoreDocs (persistent project knowledge)

At session start:

  1. Call vault_list to see available knowledge vaults.
  2. Call vault_inject_summary for any vaults relevant to the current project.
  3. Use this context to understand project architecture, decisions, and reference docs.

During the session:

  • If you create significant documentation, add it to LoreDocs with vault_add_doc.
  • Tag documents for easy cross-vault discovery with vault_tag_doc.

At session end:

  • If new docs were created or updated, ensure they are stored in LoreDocs for future sessions.

For Cowork users: Cowork does not run hooks automatically. Add instructions to call vault_list and vault_inject_summary at session start in your project CLAUDE.md.

Canonical Project Knowledge

In multi-agent environments, different tools and agents often create improvised mirrors of shared skill or configuration content -- playbooks, style guides, shared reference docs. Those mirrors drift. One agent updates the source; the other keeps reading the stale copy. Two agents in the same project end up operating from divergent knowledge with no visible signal that anything is wrong.

LoreDocs prevents this by making the vault the single canonical source that every agent reads. Instead of each agent loading a local file copy, every agent calls vault_inject_by_tag at session start and gets the same vault-managed version.

Recommended pattern

Store shared content (playbooks, team guidelines, shared specs) as vault documents rather than as local files that agents copy or mirror.

Agents load the content at session start:

vault_inject_by_tag: team-playbook

All agents -- regardless of surface (Claude Code, Cowork, CLI, or any future AI tool) -- call the same vault and receive the same current version. Updating the content requires editing the vault document once; all agents pick up the change on their next session start.

Local files (.claude/skills/, .agents/, or any surface-specific config) become pointers or bootstrap stubs only -- not the authoritative content. The vault is the source of truth.

Example: sharing a playbook across an agent team
# Session start for any agent on the team:

1. Inject the shared playbook by tag

vault_inject_by_tag("team-playbook")

2. Inject any project-specific reference docs

vault_inject_by_tag("project-architecture")

Working context is now current -- no local file copies needed.

To store the shared content in the vault (one time, or on each update):

# Store (or update) the shared playbook:

vault_update_doc(vault="team-knowledge", doc_id="playbook-id", content=open("PLAYBOOK.md").read())

Or add it fresh (path= reads directly from disk -- no need to load into context):

vault_add_doc(vault="team-knowledge", name="Team Playbook", path="/absolute/path/to/PLAYBOOK.md", tags=["team-playbook"])

Any agent that calls vault_inject_by_tag("team-playbook") reads the same document. No copies, no mirrors, no drift.

Plans: Free vs Pro

LoreDocs is local-first and free to use. Pro ($9/mo) removes the storage limits and unlocks semantic (meaning-based) retrieval. Everything runs on your machine on either plan -- Pro does not add any cloud component.

FreePro ($9/mo)
Vaults3Unlimited
Documents per vault50Unlimited
Storage500 MBUnlimited
Version history per document5 versionsUnlimited
Full-text search (FTS5)YesYes
Core MCP tools (create, search, version, tag, inject, import/export)YesYes
Local-first, no cloud, no telemetryYesYes
Semantic search (vault_search semantic=true, vault_rebuild_index)--Yes
Embedding-based document relationships (vault_find_related)Keyword co-occurrence onlyKeyword + embedding auto-links
Cross-product session linking (vault_link_session + 2 more)--Yes (also requires LoreConvo Pro)

Upgrade to Pro -- $9/month

After checkout, your license key is emailed automatically to the address you used at checkout, usually within a few minutes. Questions: info@labyrinthanalyticsconsulting.com.

Free tier limits are enforced before writes; Pro removes them. Check your current tier and usage anytime with vault_tier_status. Activate a Pro license with vault_set_tier.

The Pro semantic features use a local embedding model (BGE-small-en-v1.5) and the

LanceDB index -- still no data leaves your machine.

Features

  • Vault organization: Group docs by project with linked project metadata
  • Document versioning: Full history with rollback to any prior version
  • Tagging and categorization: Tag docs for cross-vault discovery
  • Priority levels: Mark docs as critical, high, normal, or low priority
  • Full-text search: Fast keyword search across all vaults and documents
  • Context injection: Load specific docs, tags, or vault summaries into Claude's context
  • Bulk operations: Import directories, bulk-tag, export manifests
  • Document linking: Connect related docs across vaults
  • Embedding-based document relationships (Pro): vault_find_related returns both keyword co-occurrence and embedding-based auto-links for Pro users. Uses BGE-small-en-v1.5, cosine >= 0.75, same-vault scoped. Embedding links are archived if you downgrade from Pro to Free.
  • Cross-product session linking (Pro): Automatically links vault documents to the most relevant LoreConvo sessions, and vice versa. Three tools: vault_link_session, vault_get_session_links, vault_get_linked_sessions. Requires both LoreDocs Pro and LoreConvo Pro.
  • Tier management: Free/Pro tiers with configurable limits
  • Local-first: SQLite database, no cloud dependency, zero API costs

MCP Tools

LoreDocs provides 49 MCP tools by default (50 with the notion extra installed; 51 with LOREDOCS_ENABLE_CAP_TOOLS=1 and the notion extra) organized by function:

Vault Management (8 tools)
ToolWhat it does
vault_createCreate a new vault with name and description
vault_listList all vaults with doc counts and sizes
vault_infoGet detailed vault information
vault_archiveArchive a vault (preserves data, hides from listing)
vault_deletePermanently delete a vault and all its documents
vault_link_projectLink a vault to a project directory
vault_open_workspaceOpen or create the vault scoped to a directory path
loredocs_onboardSet up workspace with starter vaults on first install
Document Operations (10 tools)
ToolWhat it does
vault_add_docAdd a new document to a vault (inline content or from file path)
vault_update_docUpdate document content (creates version history)
vault_remove_docRemove a document from a vault
vault_get_docRetrieve a document with full content
vault_list_docsList documents in a vault with filtering and sorting
vault_copy_docCopy a document to another vault
vault_move_docMove a document to another vault
vault_doc_historyView version history of a document
vault_doc_restoreRestore a document to a previous version
Search and Discovery (6 tools)
ToolWhat it does
vault_searchFull-text search across all vaults
vault_search_by_tagFind documents by tag across all vaults

| vault_find_related | Discover documents related to a given doc (Pro

只显示了开头部分;其余内容请在仓库中阅读

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