Zilliz Cloud MCP Server for LlamaIndex 10 tools — connect in under 2 minutes
LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add Zilliz Cloud as an MCP tool provider through Vinkius and your agents can query, analyze, and act on live data alongside your existing indexes.
ASK AI ABOUT THIS MCP SERVER
Vinkius supports streamable HTTP and SSE.
import asyncio
from llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI
async def main():
# Your Vinkius token. get it at cloud.vinkius.com
mcp_client = BasicMCPClient("https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")
mcp_tool_spec = McpToolSpec(client=mcp_client)
tools = await mcp_tool_spec.to_tool_list_async()
agent = FunctionAgent(
tools=tools,
llm=OpenAI(model="gpt-4o"),
system_prompt=(
"You are an assistant with access to Zilliz Cloud. "
"You have 10 tools available."
),
)
response = await agent.run(
"What tools are available in Zilliz Cloud?"
)
print(response)
asyncio.run(main())
* 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 Zilliz Cloud MCP Server
Connect your Zilliz Cloud cluster to any AI agent to automate your vector database operations. This MCP server enables your agent to manage collections, insert data, and perform high-performance similarity searches directly from natural language.
LlamaIndex agents combine Zilliz Cloud tool responses with indexed documents for comprehensive, grounded answers. Connect 10 tools through Vinkius and query live data alongside vector stores and SQL databases in a single turn. ideal for hybrid search, data enrichment, and analytical workflows.
What you can do
- Collection Management — List, describe, create, and drop vector collections in your cluster
- Memory Control — Load and release collections to optimize cluster resource usage and search availability
- Vector Search — Execute complex vector similarity searches (ANN) using customizable metrics and parameters
- Metadata Querying — Query entities using boolean expressions and metadata filters to find specific records
- Data Maintenance — Insert new vector/scalar data and delete entities from your collections
The Zilliz Cloud MCP Server exposes 10 tools through the Vinkius. Connect it to LlamaIndex 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 Zilliz Cloud to LlamaIndex via MCP
Follow these steps to integrate the Zilliz Cloud MCP Server with LlamaIndex.
Install dependencies
Run pip install llama-index-tools-mcp llama-index-llms-openai
Replace the token
Replace [YOUR_TOKEN_HERE] with your Vinkius token
Run the agent
Save to agent.py and run: python agent.py
Explore tools
The agent discovers 10 tools from Zilliz Cloud
Why Use LlamaIndex with the Zilliz Cloud MCP Server
LlamaIndex provides unique advantages when paired with Zilliz Cloud through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine Zilliz Cloud tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain Zilliz Cloud tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query Zilliz Cloud, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what Zilliz Cloud tools were called, what data was returned, and how it influenced the final answer
Zilliz Cloud + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the Zilliz Cloud MCP Server delivers measurable value.
Hybrid search: combine Zilliz Cloud real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query Zilliz Cloud to augment indexed data with live information before generating user-facing responses
Knowledge base agents: build agents that maintain and update knowledge bases by periodically querying Zilliz Cloud for fresh data
Analytical workflows: chain Zilliz Cloud queries with LlamaIndex's data connectors to build multi-source analytical reports
Zilliz Cloud MCP Tools for LlamaIndex (10)
These 10 tools become available when you connect Zilliz Cloud to LlamaIndex via MCP:
create_collection
Requires a JSON body. Create a new vector collection
delete_entities
Delete entities from a collection
describe_collection
Get details for a specific collection
drop_collection
Drop a collection
insert_entities
Insert data into a collection
list_collections
List all collections in the Zilliz cluster
load_collection
Load a collection into memory
query_entities
Query entities using metadata filtering
release_collection
Release a collection from memory
search_vectors
Requires a JSON search configuration. Perform a vector similarity search
Example Prompts for Zilliz Cloud in LlamaIndex
Ready-to-use prompts you can give your LlamaIndex agent to start working with Zilliz Cloud immediately.
"List all vector collections in my Zilliz cluster."
"Show the schema and status for collection 'text_docs'."
"Drop the collection named 'old_data_backup'."
Troubleshooting Zilliz Cloud MCP Server with LlamaIndex
Common issues when connecting Zilliz Cloud to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpZilliz Cloud + LlamaIndex FAQ
Common questions about integrating Zilliz Cloud MCP Server with LlamaIndex.
How does LlamaIndex connect to MCP servers?
Can I combine MCP tools with vector stores?
Does LlamaIndex support async MCP calls?
Connect Zilliz Cloud with your favorite client
Step-by-step setup guides for every MCP-compatible client and framework:
Anthropic's native desktop app for Claude with built-in MCP support.
AI-first code editor with integrated LLM-powered coding assistance.
GitHub Copilot in VS Code with Agent mode and MCP support.
Purpose-built IDE for agentic AI coding workflows.
Autonomous AI coding agent that runs inside VS Code.
Anthropic's agentic CLI for terminal-first development.
Python SDK for building production-grade OpenAI agent workflows.
Google's framework for building production AI agents.
Type-safe agent development for Python with first-class MCP support.
TypeScript toolkit for building AI-powered web applications.
TypeScript-native agent framework for modern web stacks.
Python framework for orchestrating collaborative AI agent crews.
Leading Python framework for composable LLM applications.
Data-aware AI agent framework for structured and unstructured sources.
Microsoft's framework for multi-agent collaborative conversations.
Connect Zilliz Cloud to LlamaIndex
Get your token, paste the configuration, and start using 10 tools in under 2 minutes. No API key management needed.