How to Use the Zilliz Cloud MCP in LangChain

Q: How do I manage collections using Zilliz Cloud and LangChain?

You can use listcollections to see what's available, then createcollection to build a new one. This gives you full control over the structure before running any searches.

Q: What is the best way to clean up data after using Zilliz Cloud and LangChain?

If you're done with a vector store, use dropcollection for a complete wipe. For just deleting specific records, run deleteentities. It’s straightforward cleanup.

Chain together complex reasoning steps using LangChain and Zilliz Cloud.

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Connect Zilliz Cloud MCP to LangChain

Create your Vinkius account to connect Zilliz Cloud to LangChain and route execution through our secure gateway. The platform manages server hosting, runtime updates, and security layers. Configuration requires no manual server provisioning.

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Setup Zilliz Cloud with LangChain

Ask AI about this MCP

ChatGPT

Claude

Perplexity

Run multi-step data queries with LangChain

The `list_collections` tool lets your AI client see every collection in the cluster. You can then use `describe_collection` to check exactly what fields a specific vector store holds. This sequence allows you to build complex logic: first list, second inspect schema, third execute the final query using `query_entities`. It keeps track of everything that happens across multiple tool calls.

Manage vectors with LangChain and MCP Server

Need to update data? Start by creating a dedicated space using `create_collection` which takes a JSON body. After the collection exists, you can use `insert_entities` to load your records. Later on, if those records need updating, simply call `delete_entities`. The overall process—setup, write, delete—is visible and manageable through LangChain's framework.

Perform vector search with LangChain

The `search_vectors` tool performs the core function: finding similarity matches. You just feed it a JSON search configuration describing what you're looking for. It takes that initial query and returns accurate results, making sure your agent has solid data to move forward with. This keeps the reasoning grounded.

Setup guide

Set up Zilliz Cloud MCP in LangChain

Prerequisites

Python 3.10+ installed
langchain-mcp-adapters + langgraph packages
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install langchain-mcp-adapters langgraph langchain-openai. The MCP adapters package converts MCP tools into native LangChain BaseTool objects.
2

Connect via HTTP transport
Use MultiServerMCPClient with "transport": "http" pointing to your Vinkius endpoint. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.
3

Create a ReAct agent
Pass the discovered tools to create_react_agent() from LangGraph. The agent automatically routes Zilliz Cloud tool calls through the MCP protocol.
4

Run with any LLM
Swap ChatOpenAI for ChatAnthropic, ChatGoogleGenerativeAI, or any LangChain-compatible model. The MCP tools work identically across all providers.

agent.py

from langchain_mcp_adapters.client import MultiServerMCPClient
from langgraph.prebuilt import create_react_agent
from langchain_openai import ChatOpenAI

async with MultiServerMCPClient({
    "zilliz-cloud-mcp": {
        "transport": "http",
        "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp",
    }
}) as client:
    tools = client.get_tools()

    agent = create_react_agent(
        ChatOpenAI(model="gpt-4o"),
        tools,
    )
    result = await agent.ainvoke({
        "messages": "List recent Zilliz Cloud transactions"
    })
    print(result["messages"][-1].content)

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Common questions about Zilliz Cloud MCP in LangChain

You can use `list_collections` to see what's available, then `create_collection` to build a new one. This gives you full control over the structure before running any searches.

This server handles vector collections, which store specialized numerical embeddings and associated metadata. It’s all about high-dimensional vector data.

Yes. By chaining tools, you can execute a process that first reads from one collection and then uses those results to inform the next tool call against another MCP Server.

If you're done with a vector store, use `drop_collection` for a complete wipe. For just deleting specific records, run `delete_entities`. It’s straightforward cleanup.

It supports metadata filtering alongside vector similarity search via the `query_entities` tool. You don't just get vectors back; you also get structured data about them.

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