How do I perform an ANN search through my agent?

Use the `search_vectors` tool by providing the collection name and a JSON float array matching the collection's dimensions. Your agent will perform an Approximate Nearest Neighbor search and return the most semantically relevant entities.

Can I filter results using structured fields instead of just vectors?

Yes. Use the `query_entities` tool with a Milvus-style filter expression. This allows you to retrieve entities based on primary keys, tags, or other scalar fields without necessarily performing a vector similarity search.

How do I check the schema and dimension requirements for a Milvus collection?

The `describe_collection` tool retrieves the complete schema mapping. Your agent will report the required vector dimensions, index types, and primary key names, helping you ensure your search queries are compatible with the database logic.

Milvus (Open-Source Vector Database) MCP Server for LlamaIndex 7 tools — connect in under 2 minutes

Name: Milvus (Open-Source Vector Database)
Availability: InStock
Author: Vinkius

docs.llamaindex.ai/en/stable/module_guides/tools/mcp

Built by Vinkius GDPR 7 Tools Framework

LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add Milvus (Open-Source Vector Database) as an MCP tool provider through Vinkius and your agents can query, analyze, and act on live data alongside your existing indexes.

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

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 Milvus (Open-Source Vector Database). "
            "You have 7 tools available."
        ),
    )

    response = await agent.run(
        "What tools are available in Milvus (Open-Source Vector Database)?"
    )
    print(response)

asyncio.run(main())

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About Milvus (Open-Source Vector Database) MCP Server

Connect your Milvus instance to any AI agent and take full control of your high-performance vector search, embedding storage, and scalar data management through natural conversation.

LlamaIndex agents combine Milvus (Open-Source Vector Database) tool responses with indexed documents for comprehensive, grounded answers. Connect 7 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

Vector Search Orchestration — Execute Approximate Nearest Neighbor (ANN) searches against your collections by providing raw embedding vectors to retrieve semantically relevant matches directly from your agent
Scalar Query Filters — Use sophisticated scalar expressions to filter entities by structured fields (e.g., tags, IDs, dates) alongside your vector search for precise data retrieval
Collection Lifecycle Audit — List all managed vector collections and retrieve detailed schema definitions, including dimensions, primary keys, and index types natively
Performance Statistics — Extract real-time metrics for your collections, including entity counts and physical memory usage, to monitor the health of your vector store
Precision Retrieval — Fetch specific vector items by their primary keys, bypassing standard semantic boundaries to audit exact data points securely
Data Management — Irreversibly delete specific vector records using primary identifiers to maintain a clean and optimized search index across your Milvus instance

The Milvus (Open-Source Vector Database) MCP Server exposes 7 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 Milvus (Open-Source Vector Database) to LlamaIndex via MCP

Follow these steps to integrate the Milvus (Open-Source Vector Database) 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 7 tools from Milvus (Open-Source Vector Database)

Why Use LlamaIndex with the Milvus (Open-Source Vector Database) MCP Server

LlamaIndex provides unique advantages when paired with Milvus (Open-Source Vector Database) through the Model Context Protocol.

Data-first architecture: LlamaIndex agents combine Milvus (Open-Source Vector Database) tool responses with indexed documents for comprehensive, grounded answers

Query pipeline framework lets you chain Milvus (Open-Source Vector Database) tool calls with transformations, filters, and re-rankers in a typed pipeline

Multi-source reasoning: agents can query Milvus (Open-Source Vector Database), a vector store, and a SQL database in a single turn and synthesize results

Observability integrations show exactly what Milvus (Open-Source Vector Database) tools were called, what data was returned, and how it influenced the final answer

Milvus (Open-Source Vector Database) + LlamaIndex Use Cases

Practical scenarios where LlamaIndex combined with the Milvus (Open-Source Vector Database) MCP Server delivers measurable value.

Hybrid search: combine Milvus (Open-Source Vector Database) real-time data with embedded document indexes for answers that are both current and comprehensive

Data enrichment: query Milvus (Open-Source Vector Database) 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 Milvus (Open-Source Vector Database) for fresh data

Analytical workflows: chain Milvus (Open-Source Vector Database) queries with LlamaIndex's data connectors to build multi-source analytical reports

Milvus (Open-Source Vector Database) MCP Tools for LlamaIndex (7)

These 7 tools become available when you connect Milvus (Open-Source Vector Database) to LlamaIndex via MCP:

delete_entities

Irreversibly delete specific vector records utilizing primary keys

describe_collection

Explore the explicit schema mapping and indexing definition of a Milvus collection

get_collection_stats

Get collection statistics bounding row counts natively

get_entities

Extract unique vector items bounding exactly by known Primary Keys

list_collections

Always query this first. List index collections tracked inside the Milvus Vector Database

query_entities

Query explicitly using scalar expressions to retrieve entities

search_vectors

Make sure to feed a strict explicit JSON Array matching exact dimensions. Search nearest vector neighbors matching implicit embedding inputs

Example Prompts for Milvus (Open-Source Vector Database) in LlamaIndex

Ready-to-use prompts you can give your LlamaIndex agent to start working with Milvus (Open-Source Vector Database) immediately.

"List all vector collections in my Milvus instance"

"Search collection 'text_knowledge_base' for vector: [0.1, -0.2, ...]"

"Show me the row count and memory stats for collection 'image_embeddings'"

Troubleshooting Milvus (Open-Source Vector Database) MCP Server with LlamaIndex

Common issues when connecting Milvus (Open-Source Vector Database) to LlamaIndex through the Vinkius, and how to resolve them.

BasicMCPClient not found

Install: pip install llama-index-tools-mcp

Milvus (Open-Source Vector Database) + LlamaIndex FAQ

Common questions about integrating Milvus (Open-Source Vector Database) MCP Server with LlamaIndex.

How does LlamaIndex connect to MCP servers?

Use the MCP client adapter to create a connection. LlamaIndex discovers all tools and wraps them as query engine tools compatible with any LlamaIndex agent.

Can I combine MCP tools with vector stores?

Yes. LlamaIndex agents can query Milvus (Open-Source Vector Database) tools and vector store indexes in the same turn, combining real-time and embedded data for grounded responses.