How do I install Qdrant?

Qdrant is a local plugin. Install it using PyPI package: mcp-qdrant and add the generated configuration snippet to your AI app's MCP config file. Then restart your AI app.

Is Qdrant safe to use?

Yes. Qdrant passed MCP Marketplace's automated security scan with a score of 10/10 (low risk). Every server on MCP Marketplace is security-scanned before it's listed; see the full security report on this page for the findings and permissions.

What credentials does Qdrant need?

Qdrant requires the following credentials or environment variables: QDRANT_URL, QDRANT_API_KEY, QDRANT_LOCAL_PATH, QDRANT_MCP_TOOLSETS, QDRANT_MCP_READ_ONLY. You can find setup instructions on the server detail page.

What AI apps work with Qdrant?

Qdrant uses the Model Context Protocol (MCP) and works with any MCP-compatible AI app, including Claude, ChatGPT / Codex, Gemini, Copilot, Cursor, and more.

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Qdrant MCP Server

by Avaazquezz

Developer ToolsLow Risk10.0MCP RegistryLocal

Free

Server data from the Official MCP Registry

MCP server that wraps the Qdrant vector database API as tools.

About

MCP server that wraps the Qdrant vector database API as tools.

Security Report

10.0

Low Risk10.0Low Risk

Valid MCP server (1 strong, 1 medium validity signals). No known CVEs in dependencies. Package registry verified. Imported from the Official MCP Registry.

5 files analyzed · 1 issue found

Security scores are indicators to help you make informed decisions, not guarantees. Always review permissions before connecting any MCP server.

Permissions Required

This plugin requests these system permissions. Most are normal for its category.

HTTP Network Access

Connects to external APIs or services over the internet.

What You'll Need

Set these up before or after installing:

Qdrant server URL, e.g. http://localhost:6333 (mutually exclusive with QDRANT_LOCAL_PATH)Optional

Environment variable: QDRANT_URL

Qdrant API key, if your instance requires oneRequired

Environment variable: QDRANT_API_KEY

Path to an embedded Qdrant instance, instead of a URLOptional

Environment variable: QDRANT_LOCAL_PATH

Comma-separated toolsets to enable: core, search, payload, snapshots, observabilityOptional

Environment variable: QDRANT_MCP_TOOLSETS

Block every tool not marked read-onlyOptional

Environment variable: QDRANT_MCP_READ_ONLY

How to Install

Add this to your MCP configuration file:

{
  "mcpServers": {
    "io-github-avaazquezz-mcp-qdrant": {
      "env": {
        "QDRANT_URL": "your-qdrant-url-here",
        "QDRANT_API_KEY": "your-qdrant-api-key-here",
        "QDRANT_LOCAL_PATH": "your-qdrant-local-path-here",
        "QDRANT_MCP_TOOLSETS": "your-qdrant-mcp-toolsets-here",
        "QDRANT_MCP_READ_ONLY": "your-qdrant-mcp-read-only-here"
      },
      "args": [
        "mcp-qdrant"
      ],
      "command": "uvx"
    }
  }
}

Documentation

View on GitHub

From the project's GitHub README.

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
`qdrant_health_check`	`core`	✅	❌	✅	Confirm the configured Qdrant instance is reachable and responding.
`qdrant_collection_create`	`core`	❌	❌	❌	Create a collection: either a single unnamed vector (`vector_size` + `distance`), or one or more named vectors (`vectors`, each a full `VectorParams` — size, distance, and optionally its own `multivector_config` for ColBERT-style multi-vectors or `quantization_config`) — exactly one of the two.
`qdrant_collection_list`	`core`	✅	❌	✅	List every collection name in the configured Qdrant instance.
`qdrant_collection_info`	`core`	✅	❌	✅	Return full config and status of one collection.
`qdrant_collection_update`	`core`	❌	❌	✅	Update optimizer/HNSW/collection/vector params on an existing collection.
`qdrant_collection_delete`	`core`	❌	✅	✅	Delete a collection and all its points; a no-op if it doesn't exist.
`qdrant_collection_exists`	`core`	✅	❌	✅	Check whether a collection exists, without raising if it doesn't.
`qdrant_points_upsert`	`core`	❌	✅	✅	Insert or replace points (id + vector + payload) in a collection.
`qdrant_points_get`	`core`	✅	❌	✅	Retrieve points by id; unknown ids are simply omitted, not an error.
`qdrant_points_delete`	`core`	❌	✅	✅	Delete points by id list or by payload filter — exactly one of the two.
`qdrant_points_scroll`	`core`	✅	❌	✅	Page through all points in a collection, optionally filtered.
`qdrant_points_count`	`core`	✅	❌	✅	Count points in a collection, optionally matching a filter.
`qdrant_query`	`core`	✅	❌	✅	Vector similarity search, with optional hybrid search over multiple prefetch stages.
`qdrant_query_batch`	`search`	✅	❌	✅	Run multiple independent queries against one collection in a single round trip — same query shapes as `qdrant_query` (plain vector or fusion+prefetch hybrid search), one per list item.
`qdrant_query_groups`	`search`	✅	❌	✅	Vector query grouped by a payload field, up to `group_size` hits per group — e.g. the best-matching chunks per source document.
`qdrant_recommend`	`search`	✅	❌	✅	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.
`qdrant_recommend_batch`	`search`	✅	❌	✅	Run multiple independent recommend queries against one collection in a single round trip.
`qdrant_recommend_groups`	`search`	✅	❌	✅	Recommend query grouped by a payload field, up to `group_size` hits per group.
`qdrant_discover`	`search`	✅	❌	✅	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.
`qdrant_discover_batch`	`search`	✅	❌	✅	Run multiple independent discover queries against one collection in a single round trip.
`qdrant_distance_matrix_pairs`	`search`	✅	❌	✅	Pairwise distance matrix between a random sample of points: for each of `sample` points, its `limit` closest neighbors among that same sample — returned as a flat list of (a, b, score) pairs.
`qdrant_distance_matrix_offsets`	`search`	✅	❌	✅	Same distance matrix as `qdrant_distance_matrix_pairs`, in a column-oriented shape (offsets into a shared id list + a parallel score array) — more compact for large samples.
`qdrant_payload_set`	`payload`	❌	❌	✅	Merge fields into the payload of selected points — exactly one of `ids`/`points_filter`.
`qdrant_payload_overwrite`	`payload`	❌	✅	✅	Replace the entire payload of selected points with `payload` — exactly one of `ids`/`points_filter`.
`qdrant_payload_delete`	`payload`	❌	✅	✅	Delete specific payload keys from selected points — exactly one of `ids`/`points_filter`.
`qdrant_payload_clear`	`payload`	❌	✅	✅	Wipe the entire payload of selected points, keeping their vectors — exactly one of `ids`/`points_filter`.
`qdrant_payload_facet`	`payload`	✅	❌	✅	Count distinct values of a payload field across the collection (or a filtered subset) — e.g. how many points per `city`.
`qdrant_payload_index_create`	`payload`	❌	❌	✅	Create a payload index on `field_name`, speeding up filters that use it.
`qdrant_payload_index_delete`	`payload`	❌	✅	✅	Delete the payload index on `field_name`.
`qdrant_collection_vector_create`	`payload`	❌	❌	✅	Add a new named vector (dense or sparse) to a collection that already has points, without touching them.
`qdrant_collection_vector_delete`	`payload`	❌	✅	✅	Remove a named vector (dense or sparse) from a collection — points keep their other vectors and payload.
`qdrant_points_batch_update`	`payload`	❌	✅	❌	Run multiple point operations (upsert, delete, set/overwrite/delete/clear payload, update/delete vectors) atomically against one collection, in the order given.
`qdrant_vectors_update`	`payload`	❌	✅	✅	Replace the vector(s) of existing points by id — leaves their payload untouched.
`qdrant_vectors_delete`	`payload`	❌	✅	✅	Remove specific named vectors from selected points, keeping their payload and other vectors — exactly one of `ids`/`points_filter`.
`qdrant_snapshot_create`	`snapshots`	❌	❌	❌	Create a snapshot of one collection's current state.
`qdrant_snapshot_list`	`snapshots`	✅	❌	✅	List the snapshots stored for one collection.
`qdrant_snapshot_delete`	`snapshots`	❌	✅	✅	Delete a collection snapshot, freeing its disk space on the server — does not touch the live collection.
`qdrant_snapshot_recover`	`snapshots`	❌	✅	✅	Overwrite `collection_name` with the state captured in a snapshot — everything written since that snapshot is lost.
`qdrant_snapshot_download`	`snapshots`	✅	❌	✅	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. `curl`) from the returned `url`.
`qdrant_storage_snapshot_create`	`snapshots`	❌	❌	❌	Create a snapshot of the whole storage (every collection and server config), not just one collection.
`qdrant_storage_snapshot_list`	`snapshots`	✅	❌	✅	List the full-storage snapshots stored on the server.
`qdrant_storage_snapshot_delete`	`snapshots`	❌	✅	✅	Delete a full-storage snapshot, freeing its disk space.
`qdrant_storage_snapshot_download`	`snapshots`	✅	❌	✅	Confirm a full-storage snapshot exists and return where to fetch it from — same caveat as `qdrant_snapshot_download`: this tool does not transfer the file itself.
`qdrant_telemetry`	`observability`	✅	❌	✅	Server-wide telemetry: build info, per-collection stats, request counters, memory and hardware usage.
`qdrant_metrics_prometheus`	`observability`	✅	❌	✅	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 `url` instead.
`qdrant_quotas_get`	`observability`	✅	❌	✅	Current server-wide resource quotas (memory/disk limits) and actual usage.
`qdrant_quotas_set`	`observability`	❌	❌	✅	Update server-wide resource quotas.
`qdrant_issues_list`	`observability`	✅	❌	✅	List the issues Qdrant has detected about its own configuration (e.g. a heavily-filtered field with no payload index).
`qdrant_issues_clear`	`observability`	❌	✅	✅	Clear all accumulated issues.

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-qdrant

In 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-qdrant

Two 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, no Bearer prefix 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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Qdrant MCP Server

About

Security Report

Findings (1)

Permissions Required

What You'll Need

How to Install

Documentation

Qdrant-MCP

Tools

Configuration

Claude Desktop / Claude Code (local, stdio)

Remote (streamable-http) — e.g. a custom connector in Claude.ai

Public "bring your own Qdrant" instance (QDRANT_MCP_BYO)

Reviews

No reviews yet

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Claude Desktop / Claude Code (local, `stdio`)

Remote (`streamable-http`) — e.g. a custom connector in Claude.ai

Public "bring your own Qdrant" instance (`QDRANT_MCP_BYO`)