OpenSearch Vector MCP Server
Run k-NN vector searches on OpenSearch — create indexes, upsert embeddings, query similar documents, and manage your vector store from any AI agent.
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What is the OpenSearch MCP Server?
The OpenSearch MCP Server gives AI agents like Claude, ChatGPT, and Cursor direct access to OpenSearch via 6 tools. Run k-NN vector searches on OpenSearch — create indexes, upsert embeddings, query similar documents, and manage your vector store from any AI agent. Powered by the Vinkius - no API keys, no infrastructure, connect in under 2 minutes.
Built-in capabilities (6)
Tools for your AI Agents to operate OpenSearch
Ask your AI agent "List all vector indexes in my OpenSearch cluster." and get the answer without opening a single dashboard. With 6 tools connected to real OpenSearch 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
OpenSearch Vector MCP Server capabilities
6 toolsknn: true` and mapping a rigid dynamic dense vector field optimized for cosine similarity. Create a new native OpenSearch KNN index ready for vector embeddings
Delete an explicit vector document bounding from OpenSearch
Retrieve explicit OpenSearch index mapping and settings
This executes a fast transactional atomic insertion into the embedding space. Upsert a singular vector document directly into an OpenSearch KNN index
List all explicit indexes residing on the OpenSearch cluster
Provide the exact index name and a JSON-stringified dense float vector array to find conceptually similar embeddings natively. Execute a K-Nearest Neighbors (k-NN) vector search against OpenSearch
What the OpenSearch Vector MCP Server unlocks
Turn your OpenSearch cluster into an AI-native vector database. Create k-NN indexes, upsert embeddings, run similarity searches, and inspect index configurations — all through natural conversation with your AI agent.
What you can do
- Vector Search — Execute k-Nearest Neighbors queries against any k-NN index with custom top-K limits and dense float vectors
- Index Management — List all cluster indexes with health status and document counts, or inspect a specific index's vector dimension, engine config, and distance metric
- Create Index — Provision new k-NN indexes optimized for cosine similarity with configurable vector dimensions (384, 768, 1536, etc.)
- Document Operations — Upsert vector documents with metadata, or delete documents from the embedding space by ID
How it works
1. Subscribe to this server
2. Enter your OpenSearch Host, Username, and Password
3. Start managing your vector store from Claude, Cursor, or any MCP-compatible client
Who is this for?
- ML engineers — test similarity queries against production embeddings without writing curl commands
- RAG developers — index and retrieve context documents for retrieval-augmented generation pipelines
- Data teams — inspect index health, document counts, and vector configurations through conversation instead of Kibana dashboards
Frequently asked questions about the OpenSearch Vector MCP Server
What vector dimensions does it support?
Any dimension supported by OpenSearch k-NN. Common values: 384 (MiniLM), 768 (BERT/all-mpnet), 1536 (OpenAI text-embedding-ada-002), 3072 (text-embedding-3-large). When creating an index, specify the exact dimension and the agent provisions the mapping automatically.
Can I delete an entire index or just individual documents?
Currently, the agent supports deleting individual documents by ID from an index. Full index deletion is not exposed through this integration to prevent accidental data loss. If you need to drop an index, use the OpenSearch Dashboards or direct API calls.
Does this work with Amazon OpenSearch Service (managed)?
Yes. Provide the Amazon OpenSearch Service endpoint as the host (e.g., https://search-xxx.us-east-1.es.amazonaws.com) along with the master username and password. The integration uses standard REST APIs that work identically on managed and self-hosted clusters.
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Connect OpenSearch Vector with your favorite client
Step-by-step setup guides for every MCP-compatible client and framework:
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Give your AI agents the power of OpenSearch MCP Server
Production-grade OpenSearch Vector MCP Server. Verified, monitored, and maintained by Vinkius. Ready for your AI agents — connect and start using immediately.