Context7
Up-to-date docs for AI agents
제작자가 쓴 설명(영어 원문)
Local QMD search and bounded index maintenance through a Streamable HTTP MCP server.
이 세 가지는 절대 하나의 점수로 합치지 않아요. 의견, 측정, 인기는 서로 다른 것이니까요.
이 서버는 아직 쉬운 말로 된 요약을 쓰지 못했어요. 아래는 레지스트리에서 읽어 온 사실이에요.
쓰고 있는 AI 앱을 고른 다음 설정을 복사하세요.
필요한 것
어떤 앱을 쓰세요?
위에서 앱을 고르세요. 기억해 두었다가 다음부터는 버튼에 “…에 설치”라고 표시해요.
Claude Desktop → Settings → Developer → Edit Config를 열고 claude_desktop_config.json에 이 내용을 붙여 넣으세요
{
"mcpServers": {
"qmd": {
"command": "docker",
"args": [
"run",
"-i",
"--rm",
"ghcr.io/x1pher/qmd-mcp:v0.2.2"
]
}
}
}터미널에서 이 명령어를 실행하세요
claude mcp add qmd -- docker run -i --rm ghcr.io/x1pher/qmd-mcp:v0.2.2프로젝트의 .cursor/mcp.json 또는 ~/.cursor/mcp.json에 붙여 넣으세요
{
"mcpServers": {
"qmd": {
"command": "docker",
"args": [
"run",
"-i",
"--rm",
"ghcr.io/x1pher/qmd-mcp:v0.2.2"
]
}
}
}브라우저가 앱을 열지 물어본 다음, 앱이 설정을 보여 주고 확인을 요청해요. 키 값은 비워 두니 직접 입력하세요.
프로젝트의 .vscode/mcp.json에 붙여 넣으세요
{
"servers": {
"qmd": {
"type": "stdio",
"command": "docker",
"args": [
"run",
"-i",
"--rm",
"ghcr.io/x1pher/qmd-mcp:v0.2.2"
]
}
}
}브라우저가 앱을 열지 물어본 다음, 앱이 설정을 보여 주고 확인을 요청해요. 키 값은 비워 두니 직접 입력하세요.
이 서버는 내 컴퓨터에서 실행되는데, ChatGPT는 인터넷에 있는 서버에만 연결할 수 있어요. 다른 앱을 고르세요.
선택한 앱은 이 기기의 쿠키에만 저장돼요. 계정은 필요 없어요.
이 설정은 제작자의 레지스트리 정보로 만들었어요. README와 다르면 README를 따르세요.
내 컴퓨터에서 실행돼요. 설치하면 AI 앱에 도구 목록이 나타나요.
누구나 볼 수 있는 정보를 점검했고, 코드는 읽지 않았어요. 해당하지 않는 항목은 개수에서 뺐어요. · 2026년 11월 7일 점검
레지스트리 이름이 GitHub 계정 X1pheR 기준으로 확인되었고, 이 계정이 저장소를 소유하고 있어요
아직 GitHub에서 이 저장소를 읽어 오지 않았어요
아직 GitHub에서 이 저장소를 읽어 오지 않았어요
내 컴퓨터에서 실행되는 서버라 인터넷 엔드포인트가 없어요
내 컴퓨터에서 실행되며, 대신 로컬 설정을 사용해요
내 컴퓨터에서 실행돼요. 도구 목록은 인터넷 엔드포인트가 있는 서버에서만 읽을 수 있어요
레지스트리에서 이 카테고리의 활성 서버 전체와 비교했어요. 스타는 게시자가 저장소를 소유한 경우에만 세요.
해당하는 기본 점검 1개 중 1개를 통과했어요. 코드는 읽지 않았어요. 키나 토큰을 넣기 전에 서버가 어떤 권한을 요구하는지 확인하고, 꼭 필요한 권한만 있는 키를 쓰세요.
레지스트리에 필수 키가 나와 있지 않아요. 다만 처음 쓸 때 로그인을 요구하는 서버도 있어요.
내 컴퓨터에서 실행돼요. AI 앱이 필요할 때 실행하고, 데이터는 제작자의 서버를 거치지 않아요(서버 자체가 온라인 서비스를 호출하는 경우는 제외).
GitHub 저장소는 마지막으로 – 변경됐고, 최신 버전(0.2.2)은 35일 전 레지스트리에 올라왔어요.
Claude Desktop, Claude Code, Cursor, VS Code처럼 MCP를 지원하는 앱에서 쓸 수 있어요. 내 컴퓨터에 Docker도 있어야 해요.
같은 카테고리에는: Context7, Codebase Memory, Exa
같은 카테고리에서 다른 제작자가 만든 서버예요. 스타 순으로 정렬했어요.
카드 보는 법:★ GitHub 스타i세 기둥: 리뷰 · 점검 · 사용 정도i업데이트 = 최신 버전을 레지스트리에 공개한 날i
Up-to-date docs for AI agents
Codebase knowledge graph for AI agents — 162 languages, sub-ms queries, 99% fewer tokens.
Connect AI agents to Exa for web search, content fetching, and multi-step research.
제작자의 저장소에서 쓰인 그대로 가져왔어요. PopNx MCP가 검토하거나 보증한 내용이 아니에요. HTML, 이미지, 스크립트는 제거했어요. 링크를 누르면 이 사이트를 벗어나요.
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.
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.
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.
mkdir -p qmd/config qmd/content
cd qmd
Put the Markdown files you want QMD to index in content/.
config/index.ymlglobal_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: 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.
compose.ymlservices:
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:
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.
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
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
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;start_embed, while embedding: false collections remain lexical-only;query runs with reranking disabled;query_reranked provides a separate CPU-heavy reranked path;source_relative_path for authoritative filesystem handoff when QMD_SOURCE_RELATIVE_ROOT is configured and the source path resolves unambiguously;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.
The container uses these stable paths:
| Path | Purpose |
|---|---|
/config/index.yml | QMD collection configuration |
/data/index.sqlite | QMD index database |
/data/home | Runtime home directory |
/data/cache | Model 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.
The Dockerfile provides working defaults for the normal runtime paths and HTTP listener. Override only the settings your deployment needs.
| Variable | Default | Purpose |
|---|---|---|
QMD_HTTP_HOST | 0.0.0.0 | HTTP listen address inside the container |
QMD_HTTP_PORT | 8181 | HTTP listen port |
QMD_CONFIG_PATH | /config/index.yml | QMD collection configuration file |
INDEX_PATH | /data/index.sqlite | QMD index database |
QMD_SOURCE_RELATIVE_ROOT | unset | Optional common source root. When set, query results include exact, collision-safe source_relative_path values relative to this root. |
QMD_DEFAULT_COLLECTION | unset | Default collection for start_embed; otherwise the first configured collection is used |
QMD_FORCE_CPU | 0 | Set to 1 to disable acceleration probing and force CPU use |
QMD_EMBED_PARALLELISM | unset | Optional QMD embedding parallelism override |
QMD_EMBED_MAX_DOCS_PER_BATCH | 8 | Default maximum documents per manual or scheduled embedding batch; accepted range 1-32 |
QMD_EMBED_MAX_BATCH_MB | 16 | Default maximum manual or scheduled embedding batch size in MiB; accepted range 1-128 |
QMD_EMBED_MAX_DURATION_MS | 3600000 | Maximum embedding session length; accepted range 60000-7200000 ms |
QMD_EMBED_INTERVAL_MINUTES | 0 | Automatic embedding check interval; 0 disables all embedding timers. Integer 0-1440 minutes |
QMD_EMBED_INITIAL_DELAY_SECONDS | 120 | First automatic embedding check delay; integer 0-3600 seconds |
QMD_REFRESH_INTERVAL_MINUTES | 15 | Scheduled index-refresh interval; refresh never starts embedding. 0 disables it, maximum 1440 |
QMD_REFRESH_INITIAL_DELAY_SECONDS | 120 | Delay 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 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.
node user.앞부분만 보여 드려요. 나머지는 저장소에서 읽어 보세요