Context7
Up-to-date docs for AI agents
Beschreibung vom Entwickler (englisches Original)
Local QMD search and bounded index maintenance through a Streamable HTTP MCP server.
Diese drei werden nie zu einer Punktzahl zusammengefasst: Meinungen, Messungen und Beliebtheit sind verschiedene Dinge.
Für diesen Server haben wir noch keine Zusammenfassung in einfacher Sprache geschrieben. Unten stehen die Fakten, die wir aus der Registry gelesen haben.
Wähle die KI-App, die du nutzt, und kopiere die Einstellungen.
Du brauchst
Welche App nutzt du?
Wähle oben deine App. Wir merken sie uns, damit der Button beim nächsten Mal „In … installieren“ anzeigt.
Öffne Claude Desktop → Settings → Developer → Edit Config und füge dies in claude_desktop_config.json ein
{
"mcpServers": {
"qmd": {
"command": "docker",
"args": [
"run",
"-i",
"--rm",
"ghcr.io/x1pher/qmd-mcp:v0.2.2"
]
}
}
}Führe das in deinem Terminal aus
claude mcp add qmd -- docker run -i --rm ghcr.io/x1pher/qmd-mcp:v0.2.2In .cursor/mcp.json in deinem Projekt oder in ~/.cursor/mcp.json einfügen
{
"mcpServers": {
"qmd": {
"command": "docker",
"args": [
"run",
"-i",
"--rm",
"ghcr.io/x1pher/qmd-mcp:v0.2.2"
]
}
}
}Dein Browser fragt, ob er die App öffnen darf, dann zeigt die App die Einstellungen und bittet dich um Bestätigung. Schlüsselwerte bleiben leer, damit du sie selbst einträgst.
In .vscode/mcp.json in deinem Projekt einfügen
{
"servers": {
"qmd": {
"type": "stdio",
"command": "docker",
"args": [
"run",
"-i",
"--rm",
"ghcr.io/x1pher/qmd-mcp:v0.2.2"
]
}
}
}Dein Browser fragt, ob er die App öffnen darf, dann zeigt die App die Einstellungen und bittet dich um Bestätigung. Schlüsselwerte bleiben leer, damit du sie selbst einträgst.
Dieser Server läuft auf deinem Computer, und ChatGPT kann sich nur mit Servern im Internet verbinden. Wähle eine andere App.
Deine Auswahl bleibt in einem Cookie auf diesem Gerät; kein Konto nötig.
Diese Einstellungen werden aus dem Registry-Eintrag des Entwicklers erzeugt. Wenn in der README etwas anderes steht, folge der README.
Läuft auf deinem Computer; die Tool-Liste erscheint nach der Installation in deiner KI-App.
Wir prüfen Informationen, die jeder sehen kann; den Code haben wir nicht gelesen. Checks, die nicht zutreffen, werden nicht mitgezählt. · Geprüft am 7. Nov. 2026
Der Registry-Name wurde mit dem GitHub-Konto X1pheR bestätigt, dem das Repo gehört
Dieses Repo haben wir noch nicht von GitHub gelesen
Dieses Repo haben wir noch nicht von GitHub gelesen
Dieser Server läuft auf deinem Computer; es gibt keinen Endpunkt im Internet
Läuft auf deinem Computer und nutzt stattdessen lokale Einstellungen
Läuft auf deinem Computer; Tool-Listen können wir nur von Endpunkten im Internet lesen
Verglichen mit allen aktiven Servern dieser Kategorie in der Registry. Sterne zählen nur, wenn dem Herausgeber das Repo gehört.
Er hat 1 von 1 zutreffenden Basischecks bestanden. Den Code haben wir nicht gelesen. Bevor du einen Schlüssel oder Token hinzufügst, sieh nach, welche Zugriffe der Server verlangt, und nutze einen Schlüssel, der nur die Berechtigungen hat, die du brauchst.
Die Registry nennt keinen erforderlichen Schlüssel, manche Server bitten dich aber bei der ersten Nutzung, dich anzumelden.
Er läuft auf deinem Computer. Deine KI-App startet ihn bei Bedarf, und die Daten laufen nicht über die Server des Entwicklers (außer der Server ruft selbst einen Onlinedienst auf).
Das GitHub-Repo wurde zuletzt – geändert, und die neueste Version (0.2.2) kam vor 35 Tagen in die Registry.
Apps, die MCP unterstützen, etwa Claude Desktop, Claude Code, Cursor und VS Code. Außerdem brauchst du Docker auf deinem Computer.
In derselben Kategorie: Context7, Codebase Memory, Exa
Server derselben Kategorie von anderen Entwicklern, sortiert nach Sternen.
So liest du eine Karte:★ GitHub-SterneiDrei Säulen: Bewertungen · Checks · NutzungiAktualisiert = neueste Version in der Registry veröffentlichti
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.
Unverändert aus dem Repo des Entwicklers übernommen. PopNx MCP hat den Inhalt weder geprüft noch empfiehlt es ihn. HTML, Bilder und Skripte wurden entfernt. Links führen von dieser Website weg.
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.Nur der Anfang wird gezeigt; den Rest findest du im Repo