Implement semantic memory layer on top of the Qdrant vector search engine
Qdrant mcp server connects Qdrant to AI assistants that speak the Model Context Protocol. Implement semantic memory layer on top of the Qdrant vector search engine.
This repository is an example of how to create a MCP server for Qdrant, a vector search engine.
An official Model Context Protocol server for keeping and retrieving memories in the Qdrant vector search engine. It acts as a semantic memory layer on top of the Qdrant database.
Once connected, the assistant can call these 3 tools directly:
Input — - information (string): Information to storeReturns — Confirmation messageTools — 1. qdrant-store - Store some information in the Qdrant database - Input: - information (string): Information to store - metadata (JSON): Optional metadata toThe server is distributed via npm as @smithery/cli, so most clients can run it without a manual build step. Add it to your MCP client's configuration and restart the client to pick it up — the copy-paste configs for Claude Desktop, Claude Code and Cursor are on this page.
Before the server will start you need to supply 6 environment variables: QDRANT_URL, QDRANT_API_KEY, COLLECTION_NAME, EMBEDDING_MODEL, QDRANT_LOCAL_PATH, FASTMCP_SERVER_HOST. Keep credentials in your client's env block or a secrets manager rather than committing them.
Database servers turn schema archaeology and ad-hoc reporting into conversation, which is why they tend to be the second or third server people install. Qdrant sits in that group, and the shape of its toolset — Input, Returns, Tools — tells you what it is really for. Worth comparing against the other databases servers in this directory before you commit to one, since several overlap in scope but differ sharply in setup cost and permissions.
| Tool | What it does |
|---|---|
| Input | - information (string): Information to store |
| Returns | Confirmation message |
| Tools | 1. qdrant-store - Store some information in the Qdrant database - Input: - information (string): Information to store - metadata (JSON): Optional metadata to store - collection_name (string): Name of the collection to st |
{
"mcpServers": {
"server-qdrant": {
"command": "npx",
"args": ["-y", "@smithery/cli"],
"env": {
"QDRANT_URL": "your-value",
"QDRANT_API_KEY": "your-value",
"COLLECTION_NAME": "your-value",
"EMBEDDING_MODEL": "your-value",
"QDRANT_LOCAL_PATH": "your-value",
"FASTMCP_SERVER_HOST": "your-value"
}
}
}
}Add to claude_desktop_config.json, then restart Claude Desktop.
| Variable | Description | Required |
|---|---|---|
| QDRANT_URL | Endpoint or connection string the server talks to. | Yes |
| QDRANT_API_KEY | Credential the server authenticates with. | Yes |
| COLLECTION_NAME | Configuration value read at startup. | Optional |
| EMBEDDING_MODEL | Configuration value read at startup. | Optional |
| QDRANT_LOCAL_PATH | Filesystem location the server is allowed to use. | Optional |
| FASTMCP_SERVER_HOST | Endpoint or connection string the server talks to. | Optional |
The simplest web tool that matters — fetch any URL and get model-ready markdown back.
Industrial-strength web extraction — render, scrape, crawl and search entire sites into clean markdown.
Puppeteer-powered browser control that drives pages from the accessibility tree instead of pixels.
Search built for AI, not humans — semantic web search that returns model-ready content, plus code context.
Answers, not links — delegate questions to Perplexity's search-grounded models and get cited responses back.
Put 6,000+ pre-built scrapers at your assistant's fingertips through one MCP endpoint.