io.github.avaazquezz/mcp-qdrant
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@io.github.avaazquezz/mcp-qdrantfind similar documents to 'MCP server setup' in the docs collection"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
Qdrant-MCP
MCP server that wraps the Qdrant vector database API as tools. See ROADMAP.md.
Tools
Generated from the live tool registry — run uv run python scripts/gen_tools_doc.py after adding or changing a tool.
Tool | Toolset | Read-only | Destructive | Idempotent | Description |
|
| ✅ | ❌ | ✅ | Confirm the configured Qdrant instance is reachable and responding. |
|
| ❌ | ❌ | ❌ | Create a collection: either a single unnamed vector ( |
|
| ✅ | ❌ | ✅ | List every collection name in the configured Qdrant instance. |
|
| ✅ | ❌ | ✅ | Return full config and status of one collection. |
|
| ❌ | ❌ | ✅ | Update optimizer/HNSW/collection/vector params on an existing collection. |
|
| ❌ | ✅ | ✅ | Delete a collection and all its points; a no-op if it doesn't exist. |
|
| ✅ | ❌ | ✅ | Check whether a collection exists, without raising if it doesn't. |
|
| ❌ | ✅ | ✅ | Insert or replace points (id + vector + payload) in a collection. |
|
| ✅ | ❌ | ✅ | Retrieve points by id; unknown ids are simply omitted, not an error. |
|
| ❌ | ✅ | ✅ | Delete points by id list or by payload filter — exactly one of the two. |
|
| ✅ | ❌ | ✅ | Page through all points in a collection, optionally filtered. |
|
| ✅ | ❌ | ✅ | Count points in a collection, optionally matching a filter. |
|
| ✅ | ❌ | ✅ | Vector similarity search, with optional hybrid search over multiple prefetch stages. |
|
| ✅ | ❌ | ✅ | Run multiple independent queries against one collection in a single round trip — same query shapes as |
|
| ✅ | ❌ | ✅ | Vector query grouped by a payload field, up to |
|
| ✅ | ❌ | ✅ | Find points similar to a set of positive examples and dissimilar to a set of negative ones (vectors or point ids) — Qdrant's recommendation API. |
|
| ✅ | ❌ | ✅ | Run multiple independent recommend queries against one collection in a single round trip. |
|
| ✅ | ❌ | ✅ | Recommend query grouped by a payload field, up to |
|
| ✅ | ❌ | ✅ | Rank points by how well they fit a target within positive/negative context pairs (vectors or point ids) — Qdrant's discovery search, a finer-grained alternative to recommend. |
|
| ✅ | ❌ | ✅ | Run multiple independent discover queries against one collection in a single round trip. |
|
| ✅ | ❌ | ✅ | Pairwise distance matrix between a random sample of points: for each of |
|
| ✅ | ❌ | ✅ | Same distance matrix as |
|
| ❌ | ❌ | ✅ | Merge fields into the payload of selected points — exactly one of |
|
| ❌ | ✅ | ✅ | Replace the entire payload of selected points with |
|
| ❌ | ✅ | ✅ | Delete specific payload keys from selected points — exactly one of |
|
| ❌ | ✅ | ✅ | Wipe the entire payload of selected points, keeping their vectors — exactly one of |
|
| ✅ | ❌ | ✅ | Count distinct values of a payload field across the collection (or a filtered subset) — e.g. how many points per |
|
| ❌ | ❌ | ✅ | Create a payload index on |
|
| ❌ | ✅ | ✅ | Delete the payload index on |
|
| ❌ | ❌ | ✅ | Add a new named vector (dense or sparse) to a collection that already has points, without touching them. |
|
| ❌ | ✅ | ✅ | Remove a named vector (dense or sparse) from a collection — points keep their other vectors and payload. |
|
| ❌ | ✅ | ❌ | Run multiple point operations (upsert, delete, set/overwrite/delete/clear payload, update/delete vectors) atomically against one collection, in the order given. |
|
| ❌ | ✅ | ✅ | Replace the vector(s) of existing points by id — leaves their payload untouched. |
|
| ❌ | ✅ | ✅ | Remove specific named vectors from selected points, keeping their payload and other vectors — exactly one of |
|
| ❌ | ❌ | ❌ | Create a snapshot of one collection's current state. |
|
| ✅ | ❌ | ✅ | List the snapshots stored for one collection. |
|
| ❌ | ✅ | ✅ | Delete a collection snapshot, freeing its disk space on the server — does not touch the live collection. |
|
| ❌ | ✅ | ✅ | Overwrite |
|
| ✅ | ❌ | ✅ | Confirm a collection snapshot exists and return where to fetch it from — this tool does not transfer the (potentially huge) snapshot file itself; download it yourself (e.g. |
|
| ❌ | ❌ | ❌ | Create a snapshot of the whole storage (every collection and server config), not just one collection. |
|
| ✅ | ❌ | ✅ | List the full-storage snapshots stored on the server. |
|
| ❌ | ✅ | ✅ | Delete a full-storage snapshot, freeing its disk space. |
|
| ✅ | ❌ | ✅ | Confirm a full-storage snapshot exists and return where to fetch it from — same caveat as |
|
| ✅ | ❌ | ✅ | Server-wide telemetry: build info, per-collection stats, request counters, memory and hardware usage. |
|
| ✅ | ❌ | ✅ | Return the URL where Qdrant serves Prometheus-format metrics — this tool does not fetch the metrics themselves (they're plain text, not JSON); point your Prometheus scraper at the returned |
|
| ✅ | ❌ | ✅ | Current server-wide resource quotas (memory/disk limits) and actual usage. |
|
| ❌ | ❌ | ✅ | Update server-wide resource quotas. |
|
| ✅ | ❌ | ✅ | List the issues Qdrant has detected about its own configuration (e.g. a heavily-filtered field with no payload index). |
|
| ❌ | ✅ | ✅ | Clear all accumulated issues. |
Related MCP server: Qdrant MCP Server
Configuration
Environment variables: QDRANT_URL, QDRANT_API_KEY, QDRANT_LOCAL_PATH (exactly one
of QDRANT_URL/QDRANT_LOCAL_PATH), QDRANT_MCP_READ_ONLY, QDRANT_MCP_TRANSPORT
(stdio default, or streamable-http), QDRANT_MCP_TOOLSETS (comma-separated;
default core only — opt in to search, payload, snapshots, observability
explicitly), QDRANT_MCP_BYO (see below — mutually exclusive with
QDRANT_URL/QDRANT_LOCAL_PATH).
Claude Desktop / Claude Code (local, stdio)
claude_desktop_config.json (Claude Desktop) or .mcp.json (Claude Code):
{
"mcpServers": {
"qdrant": {
"command": "uvx",
"args": ["mcp-qdrant"],
"env": {
"QDRANT_URL": "http://localhost:6333",
"QDRANT_MCP_TOOLSETS": "core,search"
}
}
}
}Or double-click the .mcpb bundle attached to a release — Claude Desktop prompts for the same settings through its own UI, no JSON to edit.
Remote (streamable-http) — e.g. a custom connector in Claude.ai
QDRANT_MCP_SHARED_SECRET is required in this mode — the server refuses to start
as streamable-http without one, to avoid serving an unauthenticated endpoint over the
network (verified hands-on: an open streamable-http server is trivially usable by
anyone with the URL).
QDRANT_URL=http://localhost:6333 \
QDRANT_MCP_TRANSPORT=streamable-http \
QDRANT_MCP_HTTP_HOST=0.0.0.0 \
QDRANT_MCP_SHARED_SECRET=<a long random secret> \
mcp-qdrantIn Claude.ai (Customize → Connectors → Add custom connector, verified hands-on
against a real account): enter the server's HTTPS URL, then on the detected
authentication screen choose "None" and add a Request header —
Authorization → Bearer <the same secret>.
Public "bring your own Qdrant" instance (QDRANT_MCP_BYO)
A streamable-http deployment can run with no backing Qdrant of its own — every
caller supplies their own Qdrant instance (their own Qdrant Cloud account, their
company's self-hosted Qdrant, whatever) per request, instead of using one the operator
hosts and pays for. Isolation between callers is automatic — each one talks to their own
database — so there's no shared secret, no per-user account, and no data at rest on this
server.
QDRANT_MCP_BYO=1 \
QDRANT_MCP_TRANSPORT=streamable-http \
QDRANT_MCP_HTTP_HOST=0.0.0.0 \
mcp-qdrantTwo request headers, reused for a different purpose than their name suggests — verified
hands-on that Claude.ai's custom-connector "Request headers" UI rejects made-up header
names outright unless Anthropic has approved them, so this reuses two pre-approved ones
instead of inventing X-Qdrant-Url/X-Qdrant-Api-Key:
Authorization(required) — your Qdrant URL, e.g.https://xyz.cloud.qdrant.io:6333. Sent verbatim, noBearerprefix needed.x-api-key(optional) — your Qdrant API key, if your instance needs one.
In Claude.ai: Add custom connector → authentication "None" → add both as Request headers. Your Qdrant must be reachable from the public internet — an SSRF guard rejects any URL that resolves to a private/internal/loopback address.
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