How do I find my Qdrant URL and API Key?

For **Qdrant Cloud**: Go to the Qdrant Cloud Console, select your cluster to open the Cluster Detail Page. The endpoint will be displayed there (e.g., `xyz.us-east4-0.gcp.cloud.qdrant.io`), and you can generate Database API Keys underneath it (they start with `eyJhb`). For **Self-hosted**: Provide your custom URL and the static custom key you defined in your `config.yaml`.

Can my AI use this for a RAG architecture directly?

Yes contextually, but practically your agent acts as the database debugger. It can formulate vector arrays to query `search_points`, retrieving identical payload structures. It's meant for the *engineer building the RAG*, helping you inspect distances and debug faulty retrieval mechanisms mid-code.

Does it support deleting vectors?

Yes. If an embedding got corrupted or references dropped articles, use the `delete` tool. Pass the collection name and the list of specific IDs. Qdrant handles the mutation instantly and updates the index without rebuilding.

What if I have millions of points?

Instead of overloading your chat context, instruct your agent to use the `count` tool to grasp the scale, and the `scroll` tool with a small limit constraint (e.g., 5-10 records at a time). This paginates large bodies cleanly when analyzing index health.

Qdrant MCP Server for OpenAI Agents SDK 7 tools — connect in under 2 minutes

Name: Qdrant
Availability: InStock
Author: Vinkius

openai.github.io/openai-agents-python/mcp

Built by Vinkius GDPR 7 Tools SDK

The OpenAI Agents SDK enables production-grade agent workflows in Python. Connect Qdrant through Vinkius and your agents gain typed, auto-discovered tools with built-in guardrails. no manual schema definitions required.

Get MCP Server for AI Agents

ASK AI ABOUT THIS MCP SERVER

Open in ChatGPT Open in Claude Open in Perplexity

Vinkius supports streamable HTTP and SSE.

python

import asyncio
from agents import Agent, Runner
from agents.mcp import MCPServerStreamableHttp

async def main():
    # Your Vinkius token. get it at cloud.vinkius.com
    async with MCPServerStreamableHttp(
        url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
    ) as mcp_server:

        agent = Agent(
            name="Qdrant Assistant",
            instructions=(
                "You help users interact with Qdrant. "
                "You have access to 7 tools."
            ),
            mcp_servers=[mcp_server],
        )

        result = await Runner.run(
            agent, "List all available tools from Qdrant"
        )
        print(result.final_output)

asyncio.run(main())

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Stream every event to Splunk, Datadog, or your own webhook in real-time

* Every MCP server runs on Vinkius-managed infrastructure inside AWS - a purpose-built runtime with per-request V8 isolates, Ed25519 signed audit chains, and sub-40ms cold starts optimized for native MCP execution. See our infrastructure

About Qdrant MCP Server

Connect your Qdrant vector database (Cloud or Self-Hosted) to any AI agent and bring powerful semantic retrieval and database management into your conversation.

The OpenAI Agents SDK auto-discovers all 7 tools from Qdrant through native MCP integration. Build agents with built-in guardrails, tracing, and handoff patterns. chain multiple agents where one queries Qdrant, another analyzes results, and a third generates reports, all orchestrated through Vinkius.

What you can do

Discover Collections — List all vector collections in your cluster, fetch detailed distance metrics, and monitor total payload points instantly
Semantic Vector Search — Perform nearest neighbor similarity searches. Pass a JSON array of floats and retrieve the exact payloads matching your query
Data Management — Read specific points by ID or scroll sequentially through giant datasets to debug payloads and embedding quality
Mutation Operations — Delete redundant data points safely without building separate admin scripts

The Qdrant MCP Server exposes 7 tools through the Vinkius. Connect it to OpenAI Agents SDK in under two minutes — no API keys to rotate, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.

How to Connect Qdrant to OpenAI Agents SDK via MCP

Follow these steps to integrate the Qdrant MCP Server with OpenAI Agents SDK.

Install the SDK

Run pip install openai-agents in your Python environment

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token from cloud.vinkius.com

Run the script

Save the code above and run it: python agent.py

Explore tools

The agent will automatically discover 7 tools from Qdrant

Why Use OpenAI Agents SDK with the Qdrant MCP Server

OpenAI Agents SDK provides unique advantages when paired with Qdrant through the Model Context Protocol.

Native MCP integration via `MCPServerSse`, pass the URL and the SDK auto-discovers all tools with full type safety

Built-in guardrails, tracing, and handoff patterns let you build production-grade agents without reinventing safety infrastructure

Lightweight and composable: chain multiple agents and MCP servers in a single pipeline with minimal boilerplate

First-party OpenAI support ensures optimal compatibility with GPT models for tool calling and structured output

Qdrant + OpenAI Agents SDK Use Cases

Practical scenarios where OpenAI Agents SDK combined with the Qdrant MCP Server delivers measurable value.

Automated workflows: build agents that query Qdrant, process the data, and trigger follow-up actions autonomously

Multi-agent orchestration: create specialist agents. one queries Qdrant, another analyzes results, a third generates reports

Data enrichment pipelines: stream data through Qdrant tools and transform it with OpenAI models in a single async loop

Customer support bots: agents query Qdrant to resolve tickets, look up records, and update statuses without human intervention

Qdrant MCP Tools for OpenAI Agents SDK (7)

These 7 tools become available when you connect Qdrant to OpenAI Agents SDK via MCP:

count

Counts the total number of points in a collection

delete

This action is irreversible. Deletes specific points from a collection

get_collection

Retrieves detailed information about a specific collection

get_points

Retrieves specific points by their IDs

list_collections

Lists all collections in the Qdrant instance

scroll

Returns points with their payloads. Scrolls through points in a collection, useful for pagination

search

You must provide a JSON array of floats for the query vector. Performs a nearest neighbor vector search in a collection

Example Prompts for Qdrant in OpenAI Agents SDK

Ready-to-use prompts you can give your OpenAI Agents SDK agent to start working with Qdrant immediately.

"List the configurations for all collections in my Qdrant instance."

"Count the total embedded points in the 'docs-embeddings' collection."

"Scroll and show me the IDs and payloads of the first 3 items in the 'users' collection."

Troubleshooting Qdrant MCP Server with OpenAI Agents SDK

Common issues when connecting Qdrant to OpenAI Agents SDK through the Vinkius, and how to resolve them.

MCPServerStreamableHttp not found

Ensure you have the latest version: pip install --upgrade openai-agents

Agent not calling tools

Make sure your prompt explicitly references the task the tools can help with.

Qdrant + OpenAI Agents SDK FAQ

Common questions about integrating Qdrant MCP Server with OpenAI Agents SDK.

How does the OpenAI Agents SDK connect to MCP?

Use MCPServerSse(url=...) to create a server connection. The SDK auto-discovers all tools and makes them available to your agent with full type information.