Milvus (Open-Source Vector Database) MCP Server
Manage vector storage via Milvus — perform ANN searches, query scalar entities, and audit collections.
Ask AI about this MCP Server
Vinkius supports streamable HTTP and SSE.
* 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
What is the Milvus MCP Server?
The Milvus MCP Server gives AI agents like Claude, ChatGPT, and Cursor direct access to Milvus via 7 tools. Manage vector storage via Milvus — perform ANN searches, query scalar entities, and audit collections. Powered by the Vinkius - no API keys, no infrastructure, connect in under 2 minutes.
Built-in capabilities (7)
Tools for your AI Agents to operate Milvus
Ask your AI agent "List all vector collections in my Milvus instance" and get the answer without opening a single dashboard. With 7 tools connected to real Milvus data, your agents reason over live information, cross-reference it with other MCP servers, and deliver insights you would spend hours assembling manually.
Works with Claude, ChatGPT, Cursor, and any MCP-compatible client. Powered by the Vinkius - your credentials never touch the AI model, every request is auditable. Connect in under two minutes.
Why teams choose Vinkius
One subscription gives you access to thousands of MCP servers - and you can deploy your own to the Vinkius Edge. Your AI agents only access the data you authorize, with DLP that blocks sensitive information from ever reaching the model, kill switch for instant shutdown, and up to 60% token savings. Enterprise-grade infrastructure and security, zero maintenance.
Build your own MCP Server with our secure development framework →Vinkius works with every AI agent you already use
…and any MCP-compatible client
Milvus (Open-Source Vector Database) MCP Server capabilities
7 toolsIrreversibly delete specific vector records utilizing primary keys
Explore the explicit schema mapping and indexing definition of a Milvus collection
Get collection statistics bounding row counts natively
Extract unique vector items bounding exactly by known Primary Keys
Always query this first. List index collections tracked inside the Milvus Vector Database
Query explicitly using scalar expressions to retrieve entities
Make sure to feed a strict explicit JSON Array matching exact dimensions. Search nearest vector neighbors matching implicit embedding inputs
What the Milvus (Open-Source Vector Database) MCP Server unlocks
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.
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
How it works
1. Subscribe to this server
2. Enter your Milvus Base URL and API Key (or Zilliz Cloud Token)
3. Start optimizing your vector search from Claude, Cursor, or any MCP-compatible client
Who is this for?
- ML Engineers — test vector relevance and verify embedding dimensions through natural conversation without manual SDK scripts
- Search Architects — audit collection schemas and monitor indexing performance directly from your workspace
- Software Developers — integrate AI-powered retrieval into applications and manage vector lifecycles across multiple Milvus environments efficiently
Frequently asked questions about the Milvus (Open-Source Vector Database) MCP Server
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.
More in this category
Typesense Cloud
6 toolsAutomate search cluster workflows via Typesense Cloud — monitor performance metrics, check cluster health, manage aliases, and execute multi-searches.
Railway
10 toolsEquip your AI with direct access to your Railway infrastructure — manage projects, deployments, services, and environment variables.
Figma
12 toolsConnect Figma to automate design workflows — inspect files, render layers as images, and manage comments directly from your AI agent.
You might also like
World Bank Full Access
30 toolsThe ultimate Mega-Server integration: A massive 30-tool suite granting your AI zero-auth access to the entire World Bank catalog (29,506 indicators) across all 21 specific categories.
Internet Archive Metadata
10 toolsGet detailed metadata, files, reviews, and stats for any Internet Archive item.
Levo.ai (API Security & Observability)
10 toolsSecure your APIs via Levo.ai — audit endpoints, monitor sensitive data (PII/PHI), and manage OWASP vulnerabilities.
Connect Milvus (Open-Source Vector Database) 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.
Give your AI agents the power of Milvus MCP Server
Production-grade Milvus (Open-Source Vector Database) MCP Server. Verified, monitored, and maintained by Vinkius. Ready for your AI agents — connect and start using immediately.