Qdrant MCP server with OpenAI Compatible API embedding support
Most AI and media services work still happens through a UI a human drives. Openai MCP server moves it into the conversation instead. Qdrant MCP server with OpenAI Compatible API embedding support.
The server publishes 9 tools. What each one is for:
qdrant-store — 情報とメタデータを保存qdrant-find — 意味検索による関連情報の取得OPENAI_API_KEY — OpenAI API キーOPENAI_BASE_URL — OpenAI Compatible API のベース URLOPENAI_EMBEDDING_MODEL — 埋め込みモデル（デフォルト: text-embedding-3-small）QDRANT_URL — Qdrant サーバー URL（デフォルト: http://localhost:6333）QDRANT_API_KEY — Qdrant API キーQDRANT_COLLECTION_NAME — コレクション名（デフォルト: mcp_memories）QDRANT_SEARCH_LIMIT — 検索結果の最大数（デフォルト: 5）Configuration is passed through the environment: OPENAI_API_KEY, QDRANT_URL, QDRANT_COLLECTION_NAME, QDRANT_API_KEY, OPENAI_BASE_URL. Treat anything key-shaped as a real credential — scope it to the minimum the server needs, and rotate it if it ever lands in a shared config.
The server ships on npm as @rocklerson/openai-mcp-qdrant, so your MCP client can launch it on demand — there is no separate build step. Add the server block to your client's configuration, restart it, and the tools register themselves.
This sits in the AI and media services group, where several servers overlap in what they claim to do but differ sharply once you actually set them up. Openai's toolset — qdrant-store, qdrant-find, OPENAI_API_KEY and 6 more — is a fair guide to whether it matches your workflow. It is maintained by rocklerson; worth a glance at recent repository activity before you build anything load-bearing on it.
We check each listing at SyncDev against the project's documentation before it goes live — if something here drifts out of date, it is a bug worth reporting.
| Tool | What it does |
|---|---|
| qdrant-store | 情報とメタデータを保存 |
| qdrant-find | 意味検索による関連情報の取得 |
| OPENAI_API_KEY | OpenAI API キー |
| OPENAI_BASE_URL | OpenAI Compatible API のベース URL |
| OPENAI_EMBEDDING_MODEL | 埋め込みモデル（デフォルト: text-embedding-3-small） |
| QDRANT_URL | Qdrant サーバー URL（デフォルト: http://localhost:6333） |
| QDRANT_API_KEY | Qdrant API キー |
| QDRANT_COLLECTION_NAME | コレクション名（デフォルト: mcp_memories） |
| QDRANT_SEARCH_LIMIT | 検索結果の最大数（デフォルト: 5） |
{
"mcpServers": {
"qdrant": {
"command": "npx",
"args": ["-y", "@rocklerson/openai-mcp-qdrant"],
"env": {
"OPENAI_API_KEY": "your-api-key",
"QDRANT_URL": "http://localhost:6333",
"QDRANT_COLLECTION_NAME": "claude_memories"
}
}
}
}Configuration as documented by the project. Restart the client after saving.
| Variable | Description | Required |
|---|---|---|
| OPENAI_API_KEY | Credential the server authenticates with. | Yes |
| QDRANT_URL | Endpoint or connection string the server talks to. | Yes |
| QDRANT_COLLECTION_NAME | Configuration value read at startup. | Optional |
| QDRANT_API_KEY | Credential the server authenticates with. | Yes |
| OPENAI_BASE_URL | Endpoint or connection string the server talks to. | Yes |
Build a programmable telecommunications stack for connecting telephony services with the Internet via a cloud-based utility.
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.
Give your assistant a voice — text-to-speech, voice cloning and audio tools from the ElevenLabs API.
Give your assistant a real code sandbox — isolated cloud VMs for actually running the code it writes.
The ML hub in your context window — search models, datasets, papers and run Spaces from the official server.