Skip to content
Vinkius

Zilliz Cloud Connector for AI agents.

10 live capabilities

Manage vector databases and similarity searches through natural language.

Live agent request Zilliz Cloud / Connector

Waiting for input…

AI Agent

Why people use Zilliz Cloud

Zilliz Cloud for Faster Vector Database Management

This Connector changes that by bringing the database into your chat. You just ask your agent to list your collections or describe a specific one, and it happens instantly. You get a clear view of your infrastructure without ever leaving your workspace.

  • Claude
  • ChatGPT
  • Gemini
  • Cursor
  • Visual Studio Code
  • Windsurf

What Vinkius changes

You get direct, conversational control over your vector database without writing a single line of SDK code.

Use it from Claude, ChatGPT, Cursor or another AI client you already have.

One account · 5,900+ Connectors

  1. Real-world use case 01

    Debugging collection schemas

    An AI engineer asks the agent to describe a collection to verify the dimension size and metadata fields for a new RAG pipeline.

  2. Real-world use case 02

    Rapid prototyping of embeddings

    A developer wants to see if a new set of embeddings works by running a search and viewing the results instantly in the chat.

  3. Real-world use case 03

    Cleaning up old data

    A data scientist wants to drop old backup collections to save on cluster costs using the drop_collection capability.

Complete set · 10capabilities

The complete Zilliz Cloud capability set.

These are the exact actions your AI can choose when you ask it to work with Zilliz Cloud.

Capability set01 / 03

01—04

4 capabilities in this set.

Part of 10 available through Zilliz Cloud.

  1. 01 Capability

    Create collection

    Creates a new vector collection using a provided JSON body. Use this to spin up new storage for different data types.

  2. 02 Capability

    Delete entities

    Removes specific entries from a chosen collection. This is useful for cleaning up outdated or incorrect records.

  3. 03 Capability

    Describe collection

    Pulls the schema and details for a specific vector collection. Use this to verify dimensions and metadata fields.

  4. 04 Capability

    List collections

    Shows every vector collection currently in your Zilliz cluster. This helps you keep track of all your active data sets.

Capability set02 / 03

05—07

3 capabilities in this set.

Part of 10 available through Zilliz Cloud.

  1. 05 Capability

    Drop collection

    Deletes an entire collection and its associated data from the cluster. This is the quickest way to clear out old data.

  2. 06 Capability

    Insert entities

    Adds new vector and scalar data into your collection. Use this to populate your database with new information.

  3. 07 Capability

    Load collection

    Loads a specific collection into memory for active searching. This helps you manage your cluster's active resources.

Capability set03 / 03

08—10

3 capabilities in this set.

Part of 10 available through Zilliz Cloud.

  1. 08 Capability

    Query entities

    Finds records using metadata filters and boolean expressions. This allows for precise data retrieval based on specific attributes.

  2. 09 Capability

    Release collection

    Removes a collection from memory to free up cluster resources. Use this to optimize your costs and search availability.

  3. 10 Capability

    Search vectors

    Performs a vector similarity search using a specific JSON configuration. This is how you find the most relevant items in your data.

Set up in minutes

One URL. Then ask Zilliz Cloud to work.

Claude and ChatGPT only need the Connector URL. Copy it once, add it in settings, and use Zilliz Cloud from the conversation.

Choose your client

Live preview
Advanced clients IDE · CLI

Claude · Web + desktop

Official guide ↗

Connector URL · ready to paste

Streamable HTTP
https://edge.vinkius.com/vk_preview_RKv8SXkDaOvT7RS1nwfoO7ZP8GbUdouO1cKQtzMb/mcp
  1. Step 01

    Open Connectors

    In Claude Web or Claude Desktop, open Settings and choose Connectors.

  2. Step 02

    Add the URL

    Choose Add custom connector, name it Zilliz Cloud, and paste the URL above.

  3. Step 03

    Turn it on in chat

    Select +, open Connectors, and enable Zilliz Cloud for the conversation.

Where the request belongs

Work Zilliz Cloud can move forward.

Built around the request

This is for the AI engineer who needs to test search accuracy in seconds, the data scientist monitoring cluster health without manual scripts, and the developer building RAG systems who wants to manage vector data through conversation.

01

AI Engineer

Uses this to quickly verify collection schemas and test search results during the RAG development loop.

02

Data Scientist

Uses this to monitor data distribution and manage cluster memory without writing repetitive Python scripts.

03

Backend Developer

Uses this to manage vector database operations and metadata queries during the application build phase.

Build the capability set

Add more capabilities.

Each Connector adds new actions and data without changing how you work.

Browse Connectors
Milvus (Open-Source Vector Database) logo
01 7 capabilities

Milvus (Open-Source Vector Database)

Manage vector storage via Milvus. perform ANN searches, query scalar entities, and audit collections.

View Connector
Qdrant logo
02 7 capabilities

Qdrant

Empower your AI to interact directly with your Qdrant vector database. query clusters, perform similarity searches, and manage collections effortlessly.

View Connector
Chroma (Vector DB) logo
03 7 capabilities

Chroma (Vector DB)

Manage vector embeddings via Chroma. list collections, query embeddings, and audit document counts directly from any AI agent.

View Connector
OpenSearch Vector logo
04 6 capabilities

OpenSearch Vector

Run k-NN vector searches on OpenSearch. create indexes, upsert embeddings, query similar documents, and manage your vector store from any AI agent.

View Connector
Typesense Vector Search logo
05 6 capabilities

Typesense Vector Search

Automate vector similarity searches via Typesense. index documents, manage collections, and execute semantic queries directly from your AI agent.

View Connector
Pinecone logo
06 7 capabilities

Pinecone

Equip your AI agent to manage your Pinecone vector databases. Query embeddings, fetch metrics, manage collections, and run stats natively via chat.

View Connector

Bring your own AI

Change the model, client or framework. Keep Zilliz Cloud connected.

  • Claude
  • ChatGPT
  • Gemini
  • Cursor
  • VS Code
  • Windsurf
  • ZCode
  • Cline
  • Zed
  • Continue
  • Kiro
  • Roo Code
  • Zencoder
  • Goose
  • Void
  • Augment Code
  • Amp
  • Qodo
  • Tabnine
  • Pieces
  • Sourcegraph Cody
  • JetBrains
  • Warp
  • Amazon Q
  • Antigravity
  • BoltAI
  • Raycast
  • Jan
  • LM Studio
  • AnythingLLM
  • Open WebUI
  • Msty
  • Cherry Studio
  • LibreChat
  • TypingMind
  • Chorus
  • 5ire
  • n8n
  • LangChain
  • LlamaIndex
  • CrewAI
  • Vercel AI SDK

Before you connect

Questions about Zilliz Cloud.

The practical details behind the request, access and result.

What is the Zilliz Cloud MCP for?

This Connector lets your AI agent manage your Zilliz Cloud vector database. You can perform tasks like creating collections, inserting data, and running similarity searches through natural language.

Can I use Zilliz Cloud MCP to manage my vector collections?

Yes. You can list all existing collections, create new ones with specific configurations, and even drop entire collections when they are no longer needed.

Does Zilliz Cloud MCP help with similarity searches?

Yes, it allows your agent to execute high-performance vector similarity searches using your custom metrics and configurations.

How does Zilliz Cloud MCP handle memory management?

It provides capabilities to load specific collections into memory for active use and release them when you're done, helping you optimize your cluster resources.

Can Zilliz Cloud MCP filter my data based on metadata?

Absolutely. You can query your entities using complex boolean expressions and metadata filters to find specific records quickly.

Is Zilliz Cloud MCP good for AI engineers?

It's a great fit for AI engineers because it allows for rapid prototyping of RAG systems and quick verification of search results without writing boilerplate code.

How do I find my Cluster Endpoint?

You can find your Cluster Endpoint in the Zilliz Cloud Console under the 'Cluster Details' page. It typically looks like https://in01-xxxxxxxxxxxx.vectordb.zillizcloud.com.

Why do I need to 'load' a collection before searching?

Zilliz requires collections to be loaded into memory to perform high-performance similarity searches. Use the load_collection capability to make your data available for search.

Can I filter my vector search using metadata?

Yes, Zilliz supports hybrid search. You can use the query_entities capability for metadata-only filtering or include filtering expressions in your search_vectors JSON configuration.

One connection away

Give your agent a direct line to Zilliz Cloud.

Connect Zilliz Cloud once. Keep it beside 5,900+ managed Connectors when the next task needs more.

Explore every Connector No credit card required · Free tier available