OpenSearch Vector MCP. Manage your entire vector store via conversation.
OpenSearch Vector MCP lets your AI client treat OpenSearch like a true vector database. You can create k-NN indexes for cosine similarity and manage the entire embedding workflow through conversation. Run complex similarity searches, upsert document embeddings with metadata, or inspect index health without writing any `curl` commands.
Claude 
ChatGPT 
Cursor 
Gemini 
Windsurf 
VS Code 
JetBrains 
Vercel Give Claude and any AI agent real-world access
Run k-Nearest Neighbors queries against an index using a provided embedding array to find conceptually related data.
List all existing OpenSearch indexes and retrieve detailed configuration settings for any specific index.
Provision new k-NN indexes, setting them up with required dimensions and cosine similarity optimization.
Insert or update a single vector document directly into the index along with its metadata.
Delete specific vector documents from the embedding space using their unique identifier.
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What AI agents can do with OpenSearch Vector with 6 Tools
Use these tools to create, read, update, delete, and search vector indexes directly from your AI agent.
Make your AI actually useful.
Add this MCP to Claude, Cursor, or Windsurf and your AI stops guessing. It gets real tools to look things up, take action, and handle the stuff you keep doing by hand.
Start using OpenSearch Vector MCPSearch
Executes a K-Nearest Neighbors search to find documents conceptually similar to a provided vector embedding.
List Indexes
Retrieves a list of all explicitly created indexes residing on the OpenSearch...
Get Index
Fetches detailed mapping and settings for a specific OpenSearch index name.
Index Document
Inserts or updates a single vector document directly into the OpenSearch KNN index...
Delete Document
Removes an entire vector document from the designated OpenSearch embedding space...
Create Index
Sets up a new, native OpenSearch KNN index optimized for receiving and storing vector embeddings.
Security and governance baked right in.
Pick your AI client below to get set up. Just create a Vinkius account, subscribe, and you're instantly up and running. We handle the entire backend infrastructure, delivering out-of-the-box support for HTTPS Streamable, SSE, and OAuth2—zero messy routing required.
Claude AI
Open Claude Settings
Go to claude.ai, click your profile icon, then navigate to Customize → Connectors.
Add Custom Connector
Click the "+" button and select Add custom connector. Paste your Vinkius endpoint URL:
https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp
Replace [YOUR_TOKEN_HERE] with your token
from cloud.vinkius.com. For OAuth-protected servers, expand
Advanced settings to add credentials.
Start a conversation
Open a new chat. The OpenSearch Vector integration is available immediately — no restart needed.
Choose How to Get Started
Build a custom MCP for your own tools, or connect a ready-made integration from our catalog.
Build Your Own
Turn any API into an MCP. Import a spec, define Agent Skills, or deploy with MCPFusion.
- Import from OpenAPI, Swagger, or YAML specs
- Create Agent Skills with progressive disclosure
- Deploy to edge with MCPFusion framework
- Built in DLP, auth, and compliance on each call
- Real time usage dashboard and cost metering
- Publish to catalog or keep private
Make Your AI Do More
Start with OpenSearch Vector, then connect any of our 5,200+ other servers whenever your AI needs more. One click, no limits.
- Use this MCP plus 5,200+ others, all in one place
- Add new capabilities to your AI anytime you want
- Connections are secured and governed automatically
- Track usage and costs across all your servers
- Works with Claude, ChatGPT, Cursor, and more
- New servers added to the catalog weekly
Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by OpenSearch. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.
VINKIUS CLOUD
Cloud Hosted
Managed infra
V8 Isolated
Sandboxed per request
Zero-Trust Proxy
No stored credentials
DLP Enforced
Policy on each call
GDPR Compliant
EU data residency
Token Compression
~60% cost reduction
Managing Vector Data Used To Be a Command-Line Nightmare
Every time you need to check an index's health, verify its dimension count, or provision a new space for embeddings, you used to jump through hoops. That meant writing complex `curl` commands, managing JSON payloads, and jumping between the OpenSearch dashboard and your chat window just to get basic status updates.
Now, you tell your agent what you need—like checking all available indexes with list_indexes. The system handles the API calls in the background and gives you a clean, conversational summary of document counts and health status. It’s immediate.
OpenSearch Vector MCP: Direct Similarity Search
Before this MCP, executing a conceptual search meant preparing the embedding vector array yourself and sending it in a highly structured payload. If your top-K limit was wrong or the index name changed, the whole query failed.
Now you just need to ask for it. Your agent handles the precision required for k-NN searches, making sure the correct index is targeted and the search parameters are perfect every time.
What OpenSearch Vector MCP does for your AI
Need to run semantic searches on your knowledge base? This MCP connects OpenSearch directly to your AI client, turning it into a powerful vector store. You don't have to leave your chat window to perform complex database operations. Your agent can now execute k-Nearest Neighbors queries against any index, retrieving documents based on conceptual similarity rather than keywords.
It handles the full lifecycle of vector data. Need to start fresh? You can provision new k-NN indexes optimized for specific dimensions and similarities. Later, when you have content ready, your agent will upsert those vectors with associated metadata. The whole process—from checking an index's current count to running a deep similarity search—is accessible via natural conversation.
By connecting this MCP through the Vinkius catalog, you get immediate access to robust vector management for your entire suite of AI applications.
019d75e9-e793-739b-9899-3ac45e85b9c3 
How to set up OpenSearch Vector MCP
The bottom line is that you manage and query your entire vector store through simple conversation, bypassing complex command-line interfaces.
Subscribe to this MCP and provide your OpenSearch Host, Username, and Password credentials.
Your AI client authenticates with the connection details, making all vector data operations available in a conversational context.
You instruct your agent to perform an action—like creating an index or running a search—and it executes the query against your live cluster.
Who uses OpenSearch Vector MCP
ML engineers who need to test similarity queries against production embeddings without writing curl commands. RAG developers building retrieval pipelines that require stable index management. Data teams tired of switching between chat and Kibana dashboards for basic health checks.
Indexes context documents using the MCP's create index tool, then uses search to retrieve relevant passages for generative pipelines.
Tests similarity queries against production embeddings by providing a dense float vector array and running a k-NN search.
Inspects index health, checks document counts, or provisions new indexes using the list_indexes tool instead of writing complex API calls.
Benefits of connecting OpenSearch Vector MCP
Run k-Nearest Neighbors searches without leaving your chat. Provide a dense float vector and let the MCP perform a similarity query using the search tool.
Avoid manual dashboard navigation. Use list_indexes to see all cluster indexes, check their health status, and get document counts instantly.
Build reliable RAG pipelines by provisioning new k-NN indexes with create_index, configuring specific dimensions for cosine similarity.
Keep your data clean and current. Index a single vector document using index_document or delete outdated records with delete_document.
Understand exactly what you're dealing with. Get detailed index settings and mappings for any cluster component by calling get_index.
OpenSearch Vector MCP use cases
Troubleshooting a knowledge base
A data team member needs to know which indexes exist before starting work. They simply ask the agent, and it uses list_indexes to provide an immediate overview of all available vector stores.
Building a new feature store
An ML engineer wants to test embeddings for customer feedback. Instead of writing boilerplate code, they instruct the agent to use create_index to provision a dedicated 1536-dimensional k-NN index.
Retrieving context in real time
A developer wants to answer a complex question using RAG. They pass the query embedding to the agent, which uses search to find the top 5 most similar documents from the production knowledge base index.
Cleaning up stale data
The team needs to remove old user profile embeddings that are no longer relevant. They tell the agent to use delete_document, referencing the specific document IDs for a clean sweep of obsolete vectors.
OpenSearch Vector MCP tradeoffs
What to watch out for, and the recommended way to handle each one.
Manual API calls
Opening Swagger UI or writing complex cURL commands just to check if an index exists or what its dimension is.
Just ask your agent. Use list_indexes to see every index, and then use get_index for specific details—all in conversation.
Confusing vector dimensions
Attempting to run a search using an embedding that has the wrong dimensionality (e.g., 768 when the index expects 1536).
When creating or updating embeddings, ensure you use create_index first to verify and set up the correct vector dimensions for your specific data type.
Over-relying on dashboards
Failing to notice an index is deprecated or has a health warning because they only check the Kibana dashboard.
Use list_indexes and get_index. These tools report current health status and detailed configurations directly through your agent's response.
When to use OpenSearch Vector MCP
Use this MCP if your primary need is to manage, store, or query structured vector embeddings within an existing OpenSearch cluster. This includes running k-NN searches for semantic similarity (search tool) or performing full index lifecycle management (create_index, list_indexes). Don't use it if you simply need to search unstructured text fields without embedding generation; those require a standard search engine MCP instead. Also, don't use it if your data lives in a completely different vector store type; this is strictly for OpenSearch KNN functionality.
Frequently asked questions about OpenSearch Vector MCP
How do I start with OpenSearch Vector MCP? +
Start by subscribing to this MCP and providing your OpenSearch credentials. Once connected, you can immediately use list_indexes to see what indexes are available in your cluster.
What is the difference between search and index_document using OpenSearch Vector MCP? +
The 'search' tool reads data: it takes an embedding and finds similar documents. The 'index_document' tool writes data: it takes an embedding and saves it to the cluster.
Can I create a new index with OpenSearch Vector MCP? +
Yes, you use the create_index tool. You specify if you want k-NN enabled and what vector dimensions (like 768 or 1536) the index needs.
How do I find out about an existing OpenSearch index? +
Use get_index. This tool retrieves the full mapping, settings, and engine configuration for any specific index you point it toward.
Does OpenSearch Vector MCP only handle text searches? +
No, this MCP is specifically designed for vector data. It executes k-NN searches on dense float vectors (embeddings), making it ideal for semantic similarity tasks.