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 Google ADK 7 tools — connect in under 2 minutes

Name: Qdrant
Availability: InStock
Author: Vinkius

google.github.io/adk-docs/tools/mcp-tools

Built by Vinkius GDPR 7 Tools SDK

Google Agent Development Kit (ADK) is Google's framework for building production AI agents. Add Qdrant as an MCP tool provider through Vinkius and your ADK agents can call every tool with full schema introspection.

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

from google.adk.agents import Agent
from google.adk.tools.mcp_tool import McpToolset
from google.adk.tools.mcp_tool.mcp_session_manager import (
    StreamableHTTPConnectionParams,
)

# Your Vinkius token. get it at cloud.vinkius.com
mcp_tools = McpToolset(
    connection_params=StreamableHTTPConnectionParams(
        url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp",
    )
)

agent = Agent(
    model="gemini-2.5-pro",
    name="qdrant_agent",
    instruction=(
        "You help users interact with Qdrant "
        "using 7 available tools."
    ),
    tools=[mcp_tools],
)

Fully ManagedVinkius Servers

60%Token savings

High SecurityEnterprise-grade

IAMAccess control

EU AI ActCompliant

DLPData protection

V8 IsolateSandboxed

Ed25519Audit chain

<40msKill switch

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.

Google ADK natively supports Qdrant as an MCP tool provider. declare Vinkius Edge URL and the framework handles discovery, validation, and execution automatically. Combine 7 tools with Gemini's long-context reasoning for complex multi-tool workflows, with production-ready session management and evaluation built in.

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 Google ADK 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 Google ADK via MCP

Follow these steps to integrate the Qdrant MCP Server with Google ADK.

Install Google ADK

Run pip install google-adk

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token

Create the agent

Save the code above and integrate into your ADK workflow

Explore tools

The agent will discover 7 tools from Qdrant via MCP

Why Use Google ADK with the Qdrant MCP Server

Google ADK provides unique advantages when paired with Qdrant through the Model Context Protocol.

Google ADK natively supports MCP tool servers. declare a tool provider and the framework handles discovery, validation, and execution

Built on Gemini models, ADK provides long-context reasoning ideal for complex multi-tool workflows with Qdrant

Production-ready features like session management, evaluation, and deployment come built-in. not bolted on

Seamless integration with Google Cloud services means you can combine Qdrant tools with BigQuery, Vertex AI, and Cloud Functions

Qdrant + Google ADK Use Cases

Practical scenarios where Google ADK combined with the Qdrant MCP Server delivers measurable value.

Enterprise data agents: ADK agents query Qdrant and cross-reference results with internal databases for comprehensive analysis

Multi-modal workflows: combine Qdrant tool responses with Gemini's vision and language capabilities in a single agent

Automated compliance checks: schedule ADK agents to query Qdrant regularly and flag policy violations or configuration drift

Internal tool platforms: build self-service agent platforms where teams connect their own MCP servers including Qdrant

Qdrant MCP Tools for Google ADK (7)

These 7 tools become available when you connect Qdrant to Google ADK 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 Google ADK

Ready-to-use prompts you can give your Google ADK 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 Google ADK

Common issues when connecting Qdrant to Google ADK through the Vinkius, and how to resolve them.

McpToolset not found

Update: pip install --upgrade google-adk

Qdrant + Google ADK FAQ

Common questions about integrating Qdrant MCP Server with Google ADK.

How does Google ADK connect to MCP servers?

Import the MCP toolset class and pass the server URL. ADK discovers and registers all tools automatically, making them available to your agent's tool-use loop.

Can ADK agents use multiple MCP servers?

Yes. Declare multiple MCP tool providers in your agent configuration. ADK merges all tool schemas and the agent can call tools from any server in a single turn.

Which Gemini models work best with MCP tools?

Gemini 2.0 Flash and Pro models both support function calling required for MCP tools. Flash is recommended for latency-sensitive use cases, Pro for complex reasoning.