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

QMD MCP

作者:X1pheR版本 0.2.2i★ –i在你的电脑上运行
注册名称io.github.X1pheR/qmd-mcp

安装

概览

作者提供的说明(英文原文)

Local QMD search and bounded index maintenance through a Streamable HTTP MCP server.

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: Streamable HTTP
  • 分类: 搜索与 AI 记忆

安装

选择你使用的 AI 应用,然后复制设置。

安装之前

  • 基础检查:1 项中通过 1 项
  • 在你的电脑上运行,拥有你的用户账户的权限(文件、网络、应用)。
  • 注册中心没有列出需要填写的密钥。

你需要

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": {
    "qmd": {
      "command": "docker",
      "args": [
        "run",
        "-i",
        "--rm",
        "ghcr.io/x1pher/qmd-mcp:v0.2.2"
      ]
    }
  }
}

在终端中运行这条命令

claude mcp add qmd -- docker run -i --rm ghcr.io/x1pher/qmd-mcp:v0.2.2

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": {
    "qmd": {
      "command": "docker",
      "args": [
        "run",
        "-i",
        "--rm",
        "ghcr.io/x1pher/qmd-mcp:v0.2.2"
      ]
    }
  }
}

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": {
    "qmd": {
      "type": "stdio",
      "command": "docker",
      "args": [
        "run",
        "-i",
        "--rm",
        "ghcr.io/x1pher/qmd-mcp:v0.2.2"
      ]
    }
  }
}

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。

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7日

  • ✓ 通过

    作者与仓库一致

    注册名称已通过 GitHub 账户 X1pheR 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 7286 个服务器的 0%
  • 发布到注册中心的时间比此分类中其他 7286 个服务器的 52% 更新
  • 基础检查结果等于或优于此分类中其他 7286 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

QMD MCP 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?

注册中心没有列出必需的密钥,不过有些服务器会在首次使用时要求你登录。

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(0.2.2)于36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 Docker。

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昨天更新
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★ 48,148MIT21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。

QMD MCP

QMD MCP packages QMD as a long-running Streamable HTTP MCP server. It provides QMD search and document retrieval together with bounded index-maintenance operations, without exposing arbitrary shell execution.

This is a community-maintained integration. It is not affiliated with, endorsed by, or officially maintained by the upstream QMD project.

Feedback and contributions

Use GitHub Issues for bug reports and feature requests and pull requests for proposed changes. See CONTRIBUTING.md for the development workflow, test requirements, and coding conventions. Security issues must follow the private process in SECURITY.md.

Release changes are recorded in CHANGELOG.md.

Quick start

Release images are published on GitHub Container Registry (GHCR):

ghcr.io/x1pher/qmd-mcp:v0.2.2

Published packages are public, so Docker does not need a GitHub login to pull an accepted release.

For production deployments, select the stable version tag from an accepted GitHub Release. Retain its resolved digest as immutable provenance and rollback evidence.

The image supports linux/amd64 and linux/arm64. Each platform image retains only its matching QMD native llama runtime to keep the image bounded.

1. Create the directories
mkdir -p qmd/config qmd/content

cd qmd

Put the Markdown files you want QMD to index in content/.

2. Create config/index.yml
global_context: >-

This is a local Markdown knowledge base. Search results are discovery evidence; read the source document before relying on a material claim.

collections: docs: path: /vault pattern: "**/*.md" ignore:

  • "notes/**"

notes: path: /vault/notes pattern: "**/*.md" includeByDefault: false

history: path: /vault/logs pattern: "history.md" includeByDefault: false embedding: false

path values refer to paths inside the container. The Compose example below mounts ./content at /vault.

embedding: false is a QMD MCP wrapper extension for collections that should remain lexical-only. The files are still indexed and available to explicit lexical (lex) searches, but they are excluded from embedding health and manual start_embed jobs. Use it for large append-only logs or other exact-lookup material where repeatedly rebuilding vectors adds cost without useful semantic recall.

3. Create compose.yml
services:

qmd-mcp: image: ghcr.io/x1pher/qmd-mcp:v0.2.2 container_name: qmd-mcp environment: QMD_FORCE_CPU: "1" QMD_REFRESH_INTERVAL_MINUTES: "15" QMD_REFRESH_INITIAL_DELAY_SECONDS: "120" ports:

  • "127.0.0.1:8181:8181" volumes:
  • ./content:/vault:ro
  • ./config:/config:ro
  • qmd-data:/data healthcheck: test:
  • CMD
  • node
  • -e
  • >- fetch('http://127.0.0.1:8181/health') .then(r=>process.exit(r.ok?0:1)) .catch(()=>process.exit(1)) interval: 30s timeout: 10s retries: 5 start_period: 30s restart: unless-stopped

volumes: qmd-data:

The example binds the HTTP port to loopback only. If another container must call QMD MCP directly, attach both containers to a shared Docker network and use the QMD service name instead of exposing it broadly on the host.

QMD_FORCE_CPU=1 gives a predictable CPU-only deployment. Remove it or set it to 0 if you deliberately want QMD to probe for supported acceleration.

4. Start the container
docker compose up -d

Check the service:

curl --fail http://127.0.0.1:8181/health

The Streamable HTTP MCP endpoint is:

http://127.0.0.1:8181/mcp
Docker CLI alternative

You can run the same release without Compose:

docker volume create qmd-data

docker run -d \ --name qmd-mcp \ --restart unless-stopped \ -p 127.0.0.1:8181:8181 \ -e QMD_FORCE_CPU=1 \ -e QMD_REFRESH_INTERVAL_MINUTES=15 \ -e QMD_REFRESH_INITIAL_DELAY_SECONDS=120 \ -v "$PWD/content:/vault:ro" \ -v "$PWD/config:/config:ro" \ -v qmd-data:/data \ ghcr.io/x1pher/qmd-mcp:v0.2.2

What QMD MCP provides

QMD MCP keeps QMD's read-oriented MCP tools and adds bounded administration operations:

  • health reports index and runtime state;
  • start_update starts a bounded asynchronous filesystem reindex job;
  • start_embed starts a bounded asynchronous embedding job;
  • job_status reports recent administration jobs;
  • scheduled refresh updates the lexical/index state only; embeddings run explicitly through start_embed, while embedding: false collections remain lexical-only;
  • routine query runs with reranking disabled;
  • query_reranked provides a separate CPU-heavy reranked path;
  • query results can include an exact source_relative_path for authoritative filesystem handoff when QMD_SOURCE_RELATIVE_ROOT is configured and the source path resolves unambiguously;
  • document retrieval returns internal text by default; user-visible MCP resource exposure requires both exposeToUser=true and confirmUserApprovedExposure=true, and preview/show/open/render/inspect intent is not approval.

Only one administration job runs at a time. Completed jobs are retained in memory with a bounded history. See docs/tools.md for the complete nine-tool reference, including access level and side effects.

Runtime paths

The container uses these stable paths:

PathPurpose
/config/index.ymlQMD collection configuration
/data/index.sqliteQMD index database
/data/homeRuntime home directory
/data/cacheModel and runtime cache

Source collections should normally be mounted read-only. /data must remain writable because it contains the rebuildable index and model/runtime cache.

Configuration

The Dockerfile provides working defaults for the normal runtime paths and HTTP listener. Override only the settings your deployment needs.

VariableDefaultPurpose
QMD_HTTP_HOST0.0.0.0HTTP listen address inside the container
QMD_HTTP_PORT8181HTTP listen port
QMD_CONFIG_PATH/config/index.ymlQMD collection configuration file
INDEX_PATH/data/index.sqliteQMD index database
QMD_SOURCE_RELATIVE_ROOTunsetOptional common source root. When set, query results include exact, collision-safe source_relative_path values relative to this root.
QMD_DEFAULT_COLLECTIONunsetDefault collection for start_embed; otherwise the first configured collection is used
QMD_FORCE_CPU0Set to 1 to disable acceleration probing and force CPU use
QMD_EMBED_PARALLELISMunsetOptional QMD embedding parallelism override
QMD_EMBED_MAX_DOCS_PER_BATCH8Default maximum documents per manual or scheduled embedding batch; accepted range 1-32
QMD_EMBED_MAX_BATCH_MB16Default maximum manual or scheduled embedding batch size in MiB; accepted range 1-128
QMD_EMBED_MAX_DURATION_MS3600000Maximum embedding session length; accepted range 60000-7200000 ms
QMD_EMBED_INTERVAL_MINUTES0Automatic embedding check interval; 0 disables all embedding timers. Integer 0-1440 minutes
QMD_EMBED_INITIAL_DELAY_SECONDS120First automatic embedding check delay; integer 0-3600 seconds
QMD_REFRESH_INTERVAL_MINUTES15Scheduled index-refresh interval; refresh never starts embedding. 0 disables it, maximum 1440
QMD_REFRESH_INITIAL_DELAY_SECONDS120Delay before the first scheduled refresh; accepted range 0-3600

Invalid bounded numeric values fail at startup instead of being silently accepted. QMD_SOURCE_RELATIVE_ROOT never exposes its absolute path; only a relative source path is returned, and ambiguous normalized-path collisions return null rather than guessing.

Automatic embedding

Automatic embedding is disabled by default. Set a positive QMD_EMBED_INTERVAL_MINUTES to enable periodic checks after QMD_EMBED_INITIAL_DELAY_SECONDS.

Each check selects pending work using the effective collection embedding policy. Missing embedding means enabled; default search selection and QMD_DEFAULT_COLLECTION do not limit the scheduler. One scheduled_embed job processes eligible collections sequentially under the same maintenance claim as manual jobs and refresh. Across admitted checks, the first eligible collection rotates in process memory so one collection cannot consume every shared deadline indefinitely. Busy or active-query checks skip without queuing work. Policy and pending work are rechecked before each collection.

Scheduled embedding is resumable across cooperative deadlines. Successfully embedded chunks of an unfinished document are retained as internal checkpoints for the exact model/fingerprint, remain pending until the full document is complete, and are skipped on the next scheduled attempt. Incomplete checkpoint groups are excluded from vector-search results. Manual embedding keeps the previous atomic cleanup behavior for an interrupted incomplete document. Results preserve per-collection outcomes and current aggregate pending work. Cooperative deadline cancellation is reported as deadline debt rather than a model failure; genuine embedding failures keep their existing error handling. Successful/no-op/skipped checks are quiet; incomplete runs log a compact summary without document content.

MCP health and HTTP /health expose scheduledEmbedding enablement, interval, initial delay, last outcome, next planned check and reused embedding bounds. State is process-local and resets on restart. The next check is a plan, not a promised job start. scheduledRefresh.embeddingAutomatic: false describes refresh only: refresh remains update-only.

The scheduler uses one shared cooperative time budget. Its optional AbortSignal is forwarded through the pinned SDK, embedding session and native loops. After observing abort, no new document preparation, retry or native evaluation starts. Already-started native calls are awaited, including parallel workers. Completed scheduled chunks are checkpointed for later resume instead of being recomputed; an incomplete checkpoint group is not search-visible until all expected chunks exist. Shutdown clears scheduler timers and waits for actual maintenance completion before closing the store. A cooperative deadline cannot hard-limit an in-flight native call's wall-clock duration. Manual embedding retains its per-collection session duration and atomic incomplete-document cleanup; the absent duration default is 3600000 ms.

Use one server writer per index; other replicas or direct CLI writers are outside the process-local claim. Batch bytes are a packing target, not a RAM ceiling. Deployments must supply appropriate CPU/RAM/swap limits and embedding parallelism. Query deferral applies at admission and between collections; queries arriving during a collection keep the existing behavior.

Security model

  • The container runs as the upstream Node image's unprivileged node user.
  • Source collections should normally be mounted read-only.
  • Index and cache state remain separate from source content.
  • Administration is limited to the exposed job operations. The wrapper calls the QMD store API directly; it does not invoke QMD CLI update hooks or expose arbitrary shell execution.
  • MCP request bodies are capped at 1 MiB before JSON parsing.
  • E

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