How to Use the Zilliz Cloud MCP in LlamaIndex

Q: If I need to delete old records with Zilliz Cloud and LlamaIndex?

Use the deleteentities tool. It lets you precisely target and remove specific pieces of information from a collection, maintaining the integrity of your index.

Q: Can I inspect what's in a collection using Zilliz Cloud and LlamaIndex?

Absolutely. describecollection shows you the schema details—the exact fields and data types—of any vector store before you start building with it.

Q: What is the best way to manage resources when using Zilliz Cloud and LlamaIndex?

Always remember to releasecollection when your work session is done. It frees up memory resources, keeping your application running smoothly.

Build RAG apps that ground answers in live data using LlamaIndex and Zilliz Cloud.

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Connect Zilliz Cloud MCP to LlamaIndex

Create your Vinkius account to connect Zilliz Cloud to LlamaIndex 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 LlamaIndex

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Create a searchable knowledge index with LlamaIndex

LlamaIndex uses `create_collection` to build the foundational vector store. You pass in your source data, and it becomes a structured collection within Zilliz Cloud. This allows you to run live searches against that corpus of information later on. The output from an MCP tool call immediately feeds into this index for future questioning.

Search vectors with LlamaIndex

When a user asks a question, the `search_vectors` tool performs the retrieval. It finds the most semantically related chunks of data from your knowledge base. This process is critical for RAG applications because it ensures the answer comes directly from your indexed source material, not just general model knowledge.

Manage and load data with LlamaIndex

You can use `load_collection` to pull existing vector stores into memory for immediate access. If you need to make changes after loading, the `insert_entities` tool lets you add new information. If your plan is finished, `release_collection` cleans up the in-memory resources properly.

Setup guide

Set up Zilliz Cloud MCP in LlamaIndex

Prerequisites

Python 3.10+ installed
llama-index-tools-mcp package
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install llama-index-tools-mcp llama-index-llms-openai. The MCP tools package provides BasicMCPClient and McpToolSpec.
2

Connect with BasicMCPClient
Point BasicMCPClient to your Vinkius endpoint URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. Supports SSE and Streamable HTTP transports.
3

Convert to LlamaIndex tools
Call mcp_tool_spec.to_tool_list_async() to convert all Zilliz Cloud MCP tools into native FunctionTool objects that any LlamaIndex agent can use.
4

Run with any LLM
Create a FunctionAgent with the tools and your preferred LLM. Swap OpenAI for Anthropic, Gemini, or any LlamaIndex-supported provider.

agent.py

from llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI

# Connect to the MCP
mcp_client = BasicMCPClient(
    "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)
mcp_tool_spec = McpToolSpec(client=mcp_client)

# Convert MCP tools to LlamaIndex tools
tools = await mcp_tool_spec.to_tool_list_async()

# Create and run the agent
agent = FunctionAgent(
    tools=tools,
    llm=OpenAI(model="gpt-4o"),
    system_prompt="You have access to Zilliz Cloud tools.",
)
response = await agent.run("List recent Zilliz Cloud data")

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Common questions about Zilliz Cloud MCP in LlamaIndex

LlamaIndex indexes the results of tool calls into a searchable vector store. When you query, it finds answers grounded in your live API data, eliminating hallucinations.

The server supports both metadata filtering and complex similarity searches using `query_entities` or `search_vectors`. You get both the vector match and the associated structured data.

Use the `delete_entities` tool. It lets you precisely target and remove specific pieces of information from a collection, maintaining the integrity of your index.

Absolutely. `describe_collection` shows you the schema details—the exact fields and data types—of any vector store before you start building with it.

Always remember to `release_collection` when your work session is done. It frees up memory resources, keeping your application running smoothly.

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