MCP For Claude Marketplace
OpenSearch Vector

OpenSearch Vector MCP Connector for Claude

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Run k-NN vector searches on OpenSearch — create indexes, upsert embeddings, query similar documents, and manage your vector store from any AI agent.

6 tools Official Updated Jun 28, 2026 Official Vinkius Partner
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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
vector-databasek-nnsearch-engineembeddingsindexing

6 tools expose this connector's capabilities to your AI agent.

search

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

list_indexes

List all explicit indexes residing on the OpenSearch cluster

get_index

Retrieve explicit OpenSearch index mapping and settings

index_document

This executes a fast transactional atomic insertion into the embedding space. Upsert a singular vector document directly into an OpenSearch KNN index

delete_document

Delete an explicit vector document bounding from OpenSearch

create_index

knn: true` and mapping a rigid dynamic dense vector field optimized for cosine similarity. Create a new native OpenSearch KNN index ready for vector embeddings

See how to talk to your AI agent using OpenSearch Vector.

List all vector indexes in my OpenSearch cluster.

Found 4 indexes. 1) 'product-embeddings' — 45,230 docs, green health, 768 dimensions. 2) 'support-tickets' — 12,800 docs, green, 1536 dimensions. 3) 'knowledge-base' — 3,450 docs, green, 384 dimensions. 4) 'test-index' — 50 docs, yellow, 768 dimensions. Want me to inspect the mapping of any of these?

Find the 5 most similar documents to this embedding in the knowledge-base index.

Top 5 results from 'knowledge-base' (cosine similarity): 1) 'API Rate Limiting Guide' — score: 0.94. 2) 'Authentication Best Practices' — score: 0.89. 3) 'Error Handling Documentation' — score: 0.87. 4) 'SDK Migration Guide v3' — score: 0.83. 5) 'Webhook Configuration' — score: 0.81. Want me to pull the full content of the top result?

Create a new k-NN index called 'customer-feedback' with 1536 dimensions.

Index 'customer-feedback' created successfully. Configuration: k-NN enabled, 1536 dimensions, cosine similarity, NMSLIB engine. The index is empty and ready for document ingestion. Want me to upsert a test document to verify the mapping?

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.

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