Qdrant Embedding Search
БесплатноНе проверенMCP server that searches documents in Qdrant using embeddings from LMStudio. Takes a text query, converts it to a vector via LMStudio's OpenAI-compatible API, a
Описание
MCP server that searches documents in Qdrant using embeddings from LMStudio. Takes a text query, converts it to a vector via LMStudio's OpenAI-compatible API, and performs semantic search in Qdrant.
README
MCP server that searches documents in Qdrant using embeddings from LMStudio.
Takes a text query, converts it to a vector via LMStudio's OpenAI-compatible API, and performs semantic search in Qdrant.
Prerequisites
- Node.js 18+
- LMStudio running with an embedding model loaded (default:
text-embedding-qwen3-embedding-4b) - Qdrant running with a collection containing your documents
Usage
With npx
{
"mcpServers": {
"qdrant-docs": {
"command": "npx",
"args": ["-y", "mcp-qdrant-embedding-search"],
"env": {
"QDRANT_URL": "http://localhost:6333",
"QDRANT_COLLECTION": "my_docs",
"LMSTUDIO_URL": "http://localhost:1234"
}
}
}
}
With node (local)
git clone https://github.com/plixplox/mcp-qdrant-embedding-search.git
cd mcp-qdrant-embedding-search
npm install
npm run build
{
"mcpServers": {
"qdrant-docs": {
"command": "node",
"args": ["/path/to/mcp-qdrant-embedding-search/dist/index.js"],
"env": {
"QDRANT_COLLECTION": "my_docs"
}
}
}
}
Tools
search_docs
Search documents by semantic similarity.
| Parameter | Type | Required | Description |
|---|---|---|---|
query |
string | yes | Search query text |
limit |
number | no | Max results (default: 5) |
collection |
string | no | Qdrant collection (default: from config) |
list_collections
List all available Qdrant collections. No parameters.
Configuration
All settings are configured via environment variables:
| Variable | Default | Description |
|---|---|---|
LMSTUDIO_URL |
http://localhost:1234 |
LMStudio server URL |
LMSTUDIO_EMBEDDING_MODEL |
text-embedding-qwen3-embedding-4b |
Embedding model name |
QDRANT_URL |
http://localhost:6333 |
Qdrant server URL |
QDRANT_API_KEY |
— | Qdrant API key (optional) |
QDRANT_COLLECTION |
documents |
Default collection to search |
SEARCH_LIMIT |
5 |
Default number of results |
TOOL_SEARCH_NAME |
search_docs |
Custom name for the search tool |
TOOL_SEARCH_DESCRIPTION |
Search documentation by semantic similarity... |
Custom description for the search tool |
TOOL_LIST_NAME |
list_collections |
Custom name for the list tool |
TOOL_LIST_DESCRIPTION |
List all available Qdrant collections |
Custom description for the list tool |
Custom tool descriptions
Tool names and descriptions are visible to the LLM and affect when it decides to call them. Customize them to match your use case:
{
"env": {
"TOOL_SEARCH_NAME": "search_api_reference",
"TOOL_SEARCH_DESCRIPTION": "Search the REST API reference. Use when you need endpoint specs, request/response schemas, or auth details."
}
}
License
ISC
Установка Qdrant Embedding Search
У этого сервера нет опубликованного пакета — он собирается из исходников. Открой репозиторий и следуй инструкции в README.
▸ github.com/plixplox/mcp-qdrant-embedding-searchFAQ
Qdrant Embedding Search MCP бесплатный?
Да, Qdrant Embedding Search MCP бесплатный — установка в пару кликов через Unyly без оплаты.
Нужен ли API-ключ для Qdrant Embedding Search?
Нет, Qdrant Embedding Search работает без API-ключей и переменных окружения.
Qdrant Embedding Search — hosted или self-hosted?
Self-hosted: сервер запускается локально на твоей машине командой из раздела установки.
Как установить Qdrant Embedding Search в Claude Desktop, Claude Code или Cursor?
Открой Qdrant Embedding Search на unyly.org, выбери вкладку своего клиента (Claude Desktop, Claude Code, Cursor) и нажми Install — конфиг сгенерируется автоматически, без правки JSON.
Похожие MCP
Fetch
Web content fetching and conversion for efficient LLM usage.
AWS KB Retrieval
Retrieval from AWS Knowledge Base using Bedrock Agent Runtime.
автор: modelcontextprotocolSpring AI MCP Server
Provides auto-configuration for setting up an MCP server in Spring Boot applications.
llm-analysis-assistant
A very streamlined mcp client that supports calling and monitoring stdio/sse/streamableHttp, and can also view request responses through the /logs page. It also
автор: xuzexin-hzCompare Qdrant Embedding Search with
Не уверен что выбрать?
Найди свой стек за 60 секунд
Автор?
Embed-бейдж для README
Похожее
Все в категории ai
