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MCP Server Qdrant

by qdrant

Stores and retrieves semantic memories in Qdrant using configurable embeddings, collections, and local or remote database connections.

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Install MCP Server Qdrant

Pick your client, then add it in one click or paste the config.

STDIOPython
~/Library/Application Support/Claude/claude_desktop_config.json
claude_desktop_config.json
{
  "mcpServers": {
    "qdrant-mcp-server-qdrant": {
      "command": "uvx",
      "args": [
        "mcp-server-qdrant"
      ],
      "env": {
        "QDRANT_URL": "YOUR_VALUE_HERE",
        "QDRANT_API_KEY": "YOUR_VALUE_HERE",
        "QDRANT_LOCAL_PATH": "YOUR_VALUE_HERE",
        "COLLECTION_NAME": "YOUR_VALUE_HERE",
        "EMBEDDING_PROVIDER": "YOUR_VALUE_HERE",
        "EMBEDDING_MODEL": "YOUR_VALUE_HERE",
        "QDRANT_SEARCH_LIMIT": "YOUR_VALUE_HERE",
        "QDRANT_READ_ONLY": "YOUR_VALUE_HERE"
      }
    }
  }
}

Settings → Developer → Edit Config, merge this into mcpServers, then fully quit and reopen Claude Desktop.

Values are written into the snippet above so you can paste it as is. They never leave your browser.

Install not yet sandbox-tested— we haven't run this command through our automated sandbox yet. Not a red flag; we're working through the catalog.

Overview

The qdrant/mcp-server-qdrant MCP server connects MCP clients to Qdrant for storing and retrieving semantic memories. It encodes stored information with a FastEmbed model, saves it in a selected Qdrant collection, and returns relevant results for natural-language queries. Configure it with either a Qdrant URL or a local database path, plus optional API-key and collection settings. Reach for it when an agent needs persistent vector-backed memory or semantic retrieval.

Use cases

  • Store agent memories in a Qdrant collection
  • Search stored information with natural-language queries
  • Attach JSON metadata to saved memories
  • Run semantic retrieval against a local Qdrant database
  • Expose Qdrant over SSE or streamable HTTP

Key features

  • Qdrant-backed semantic memory
  • FastEmbed model configuration
  • Remote URL and local-path connection modes
  • Optional metadata and collection selection
  • Read-only mode
  • stdio, SSE, and streamable HTTP transports

Tools4

What MCP Server Qdrant lets your agent do. Expand a tool for its parameters.

Self-reported~10 tokens of context

Security & health

What it can reach and how reliably it runs — from our own checks, not the maintainer’s claims.

ActiveLast checked Today

Recent health check succeeded.

30/31 checks healthy over the last 77d

Supply-chain signal

No high-severity advisories surfaced by our automated scan.

Critical 0High 0Medium 0Low 0

Scanned 7d ago via OSV.dev · mcp-server-qdrant (PyPI)

Adoption & maintenance

Factual signals from GitHub, npm, and our automated checks — not a rating.

GitHub stars1.6kStargazers on the source repository.
Last commit1mo agoMost recent push to the default branch.
Tools exposed4Callable tools this server registers over MCP.
Directory activity8 viewsConfig copies, upvotes, and views on AllMCPs.

How MCP Server Qdrant works

What qdrant/mcp-server-qdrant MCP server does

The qdrant/mcp-server-qdrant MCP server provides an MCP interface to Qdrant, a vector search engine. Its documented purpose is to maintain and retrieve memories using a semantic memory layer over a Qdrant database. Information is converted into vectors with an embedding model and stored in a named collection, where it can later be searched with a text query.

The server can work with a Qdrant instance reachable through a URL or with a local Qdrant database path. It can also create the configured collection automatically when that collection does not already exist.

How it works

A client sends information to the storage tool, optionally including JSON metadata and a collection name. The server embeds the information and writes it to Qdrant. A separate search tool accepts a query and collection name, then returns matching stored information as separate messages.

The default embedding provider is FastEmbed, and the default model is sentence-transformers/all-MiniLM-L6-v2. FastEmbed is currently the only supported embedding provider. Search returns up to 10 results by default, with the limit controlled through configuration.

The qdrant/mcp-server-qdrant MCP server supports standard input/output transport by default. It also supports Server-Sent Events and streamable HTTP, which can be selected with the --transport option for clients connecting remotely.

Setup and configuration

Run the server with uvx mcp-server-qdrant, or build and run the provided Dockerfile. A Qdrant URL and, when required by that deployment, an API key can be supplied through environment variables. Local mode instead uses QDRANT_LOCAL_PATH; the URL and local path must not be configured together.

COLLECTION_NAME sets the default collection. If no default is configured, storage and search requests must provide a collection name. EMBEDDING_MODEL changes the FastEmbed model, while QDRANT_READ_ONLY=true disables the storage tool.

For SSE or streamable HTTP, use --transport sse or the corresponding transport value and configure the server port with FASTMCP_SERVER_PORT. Container deployments should set FASTMCP_SERVER_HOST=0.0.0.0 so the process listens on the container's network interfaces.

Tools and capabilities

The documented MCP operations cover:

  • Storing information in Qdrant with optional JSON metadata.
  • Searching Qdrant with a text query.
  • Selecting or using a collection name.
  • Reporting information and metadata through the exposed MCP interface.
  • Limiting the number of returned search results.
  • Disabling writes for read-only deployments.

The README names the primary operations qdrant-store and qdrant-find; the listing exposes the related inputs as information, metadata, collection_name, and query.

Limitations and notes

Only FastEmbed is supported as the embedding provider at present. The server requires either a Qdrant URL or a local Qdrant database path, and those two connection modes cannot be used simultaneously. API-key authentication is relevant to Qdrant deployments that require it, but local Qdrant mode can be configured without a Qdrant URL or API key.

The default transport is intended for local MCP clients, while SSE and streamable HTTP are available for remote clients. The documented desktop-client setup specifically covers Claude Desktop.

Read the full README View source on GitHub

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Frequently Asked Questions about MCP Server Qdrant

Run `uvx mcp-server-qdrant`. No separate installation is required when using uvx.

Yes. The README provides manual Claude Desktop configuration and an installation path through Smithery.

Set `QDRANT_LOCAL_PATH` to the database path and configure `COLLECTION_NAME` as needed. Do not set `QDRANT_URL` at the same time.

Set `QDRANT_READ_ONLY=true`; this disables the `qdrant-store` tool.

It currently supports FastEmbed models, using `sentence-transformers/all-MiniLM-L6-v2` by default.

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