Search, upsert, and manage vector collections in your Qdrant database — AI-native vector search for semantic retrieval and RAG.
Use with AI AssistantsMCP
Connect Claude, Cursor, or any MCP-compatible client — then call tools directly
① Add This MCP Server
Paste into your AI client config — then all its tools are available instantly.
{
"mcpServers": {
"qdrant": {
"url": "https://mcp.aerostack.dev/s/aerostack/mcp-qdrant",
"headers": {
"Authorization": "Bearer YOUR_AEROSTACK_TOKEN"
}
}
}
}Replace YOUR_AEROSTACK_TOKEN with your API token from the dashboard.
② Call a Tool
Ask your AI assistant to call a specific tool, or send raw JSON-RPC:
Natural Language Prompt
“Use the _ping tool to verify qdrant connectivity by querying the collections endpoint. used internally by aerostack to validate credentials”
Using a Workspace?
Add this MCP to your Workspace — your team shares one token, secrets are stored securely, and every AI agent in the workspace can call it without per-user setup.
Search, upsert, and manage vector collections in your Qdrant database from your AI agents.
Qdrant is the open-source vector search engine built for AI applications — powering semantic search, RAG pipelines, and recommendation systems at scale. This MCP server gives your AI agents direct access to your Qdrant instance: creating collections, upserting vectors with payloads, running similarity searches, and managing points — all through natural language.
Live endpoint: https://mcp.aerostack.dev/s/aerostack/mcp-qdrant
| Tool | Description |
|---|---|
list_collections | List all collections in the database |
get_collection | Get collection info including vector count, config, and status |
create_collection | Create a new collection with vector size and distance metric |
delete_collection | Delete a collection and all its data |
upsert_points | Upsert points (vectors + payload) into a collection |
search | Search for similar vectors with filters and score thresholds |
get_points | Get points by their IDs |
delete_points | Delete points by IDs or filter |
scroll | Scroll through points with optional filter and pagination |
count | Count points in a collection with optional filter |
| Variable | Required | Description | How to Get |
|---|---|---|---|
QDRANT_URL | Yes | Your Qdrant instance URL (e.g. https://xyz.us-east4-0.gcp.cloud.qdrant.io:6333) | cloud.qdrant.io → Your Cluster → copy Cluster URL |
QDRANT_API_KEY | Yes | Qdrant API key for authentication | cloud.qdrant.io → Your Cluster → Data Access Control → create or copy API Key |
QDRANT_URL and QDRANT_API_KEY under Project → SecretsOnce added, every AI agent in your workspace can call Qdrant tools automatically — no per-user setup needed.
"List all collections in my Qdrant database"
"Create a new collection called 'documents' with 1536-dimensional vectors using cosine distance"
"Search the 'documents' collection for vectors similar to this embedding, return top 5 results"
"Count how many points are in the 'products' collection where category is 'electronics'"
curl -X POST https://mcp.aerostack.dev/s/aerostack/mcp-qdrant \
-H 'Content-Type: application/json' \
-H 'X-Mcp-Secret-QDRANT-URL: https://your-cluster.cloud.qdrant.io:6333' \
-H 'X-Mcp-Secret-QDRANT-API-KEY: your-api-key' \
-d '{"jsonrpc":"2.0","id":1,"method":"tools/call","params":{"name":"list_collections","arguments":{}}}'
MIT
Live Endpoint
https://mcp.aerostack.dev/s/aerostack/mcp-qdrant
Sub-50ms globally · Zero cold start
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Yes. Use create_collection to define a new Qdrant collection, upsert_points to index vectors, and search to run semantic similarity queries — all from a single Claude conversation.
The search tool accepts a payload filter alongside the query vector, so Claude can scope results to a subset of Qdrant points by metadata fields without a separate filtering step.
delete_collection removes the collection and all its points permanently in Qdrant. There is no soft-delete; use get_collection to verify contents before issuing the command.
Yes. The scroll tool fetches points in pages using an offset cursor. Chain multiple scroll calls to iterate through an entire Qdrant collection without loading everything into memory at once.