Milvus MCP Server
Connect AI assistants to Milvus, the open-source vector database, for semantic search, vector storage, and hybrid query operations through the Model Context Protocol.
Overview
The Milvus MCP Server enables AI assistants to interact with Milvus, the high-performance open-source vector database built for scalable similarity search and AI applications. Through the Model Context Protocol, AI models can create and manage vector collections, perform semantic search, insert data, and manage indexes — making Milvus's vector storage capabilities available directly through natural language.
Official Server:
Developed by Zilliz, the team behind Milvus
Key Features
Vector Search
Perform high-dimensional vector similarity search across collections with configurable top-k results.
Collection Management
Create, describe, list, and drop collections with custom schemas and vector dimensions.
Data Ingestion
Insert vectors and associated metadata into collections for retrieval and search.
Index Management
Create and manage vector indexes to optimize search performance at scale.
Hybrid Search
Combine vector similarity with attribute filtering for precise, context-aware results.
Cloud & Self-Hosted
Connect to Milvus Lite, standalone deployments, or fully managed Zilliz Cloud clusters.
Available Tools
Quick Reference
| Tool | Purpose | Category |
|---|---|---|
create_collection | Create a new collection with schema definition | Schema |
describe_collection | Get collection details and statistics | Discovery |
list_collections | List all collections in the database | Discovery |
drop_collection | Delete a collection and its data | Schema |
insert_data | Insert vectors with metadata into a collection | Write |
vector_search | Search for similar vectors with optional filtering | Search |
create_index | Build an index for faster vector search | Indexing |
Detailed Usage
create_collection▶
Create a new collection with a defined schema including vector field dimension and optional metadata fields.
use_mcp_tool({
server_name: "milvus",
tool_name: "create_collection",
arguments: {
collection_name: "documents",
dimension: 1536,
description: "Document embeddings collection"
}
});
vector_search▶
Search for the most similar vectors in a collection, with optional metadata filtering.
use_mcp_tool({
server_name: "milvus",
tool_name: "vector_search",
arguments: {
collection_name: "documents",
vector: [0.1, 0.2, ...],
top_k: 10,
filter: "category == 'research'"
}
});
insert_data▶
Insert vectors with associated metadata into a collection for future search and retrieval.
use_mcp_tool({
server_name: "milvus",
tool_name: "insert_data",
arguments: {
collection_name: "documents",
vectors: [[0.1, 0.2, ...], [0.3, 0.4, ...]],
metadata: [
{ text: "First document", source: "pdf" },
{ text: "Second document", source: "web" }
]
}
});
create_index▶
Create an index on a collection to accelerate vector similarity search.
use_mcp_tool({
server_name: "milvus",
tool_name: "create_index",
arguments: {
collection_name: "documents",
index_type: "IVF_FLAT",
metric_type: "IP",
params: { nlist: 1024 }
}
});
Installation
{
"mcpServers": {
"milvus": {
"command": "uvx",
"args": [
"mcp-server-milvus"
],
"env": {
"MILVUS_URI": "http://localhost:19530"
}
}
}
}
Connection Options:
Set MILVUS_URI to your Milvus server address. Use http://localhost:19530 for local Milvus, or your Zilliz Cloud endpoint for managed deployments. Add MILVUS_TOKEN for authentication when needed.
Common Use Cases
- AI-Powered RAG Pipelines: Connect vector stores to LLMs for retrieval-augmented generation with semantic search
- Document & Media Search: Index and query embeddings from documents, images, or audio for intelligent retrieval
- Recommendation Systems: Build vector-based similarity search for product or content recommendations
- Multi-Agent Knowledge Bases: Share a common vector store across multiple AI agents for consistent context
Sources
Related Articles
Setting up MCP Servers in Claude Code
Learn how to configure Model Context Protocol (MCP) servers in Anthropic's Claude Code CLI to supercharge your AI-assisted development workflow with external tools and services.
Thirdweb MCP Server
Thirdweb MCP servers enable AI agents to interact with EVM blockchains via thirdweb services, supporting multi-chain data querying, wallet and contract operations, autonomous execution, and both hosted and self-hosted deployment options.
MySQL MCP Server
MySQL MCP servers enable AI models to interact with MySQL databases, providing capabilities for structured data operations, SQL queries, transaction management, and relational data management.