MCP For Claude Marketplace
Elasticsearch Vector

Elasticsearch Vector MCP Connector for Claude

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Empower vector search via Elasticsearch — perform dense vector kNN searches, handle index mappings, and index embedding documents directly from any AI agent.

6 tools Official Updated Jun 28, 2026 Official Vinkius Partner
More Details Connect to Claude

Connect your Elasticsearch cluster to any AI agent and take full control of your vector search and semantic discovery workflows through natural conversation.

What you can do

  • AI-Powered Vector Search — Perform raw K-Nearest Neighbors (kNN) computations mapping absolute semantic similarity across multi-dimensional embedding arrays
  • Index Orchestration — Enumerate active storage namespaces and validate physical Elasticsearch clusters tracking explicit dimensional shards securely
  • Schema Management — Analyze specific index mapping rules and provision strictly typed data structures enforcing numeric dimensions for cluster readiness
  • Document Indexing — Command synchronous bulk insertions attaching exact dense_vector embedding payloads to persist data into raw Lucene partitions
  • Data Invalidation — Enforce immediate hard document vaporization finding specific exact UUIDs stripping records from physical indices seamlessly
  • Metadata Auditing — Analyze dimensional constraints and matching similarity thresholds perfectly to verify your vector search configurations

How it works

  1. Subscribe to this server
  2. Enter your Elasticsearch Host URL and API Key (found in Kibana > Stack Management > Security > API Keys)
  3. Start managing your vector search from Claude, Cursor, or any MCP-compatible client

Who is this for?

  • AI Engineers — perform semantic searches and test embedding models without writing complex query DSL
  • Software Developers — index embedding documents and verify kNN search results directly from the IDE or chat
  • Data Scientists — monitor vector index mappings and verify dimensional constraints using natural language
  • Ops Teams — verify cluster index health and manage vector storage namespaces in real-time
vector-searchknn-searchembeddingssemantic-searchindexingai-infrastructure

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

search

Dense vector knn search

list_indexes

List all indexes

get_index

Get index info

index_document

Index a document

delete_document

Delete a document

create_index

Create dense_vector index

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

Perform a kNN search in index 'product-embeddings' with vector [0.1, 0.2, ...]

Searching 'product-embeddings'... I found the top 5 most similar documents. Result #1: 'Leather Backpack' (Similarity: 0.98). Result #2: 'Canvas Tote' (Similarity: 0.92). Would you like the full metadata for these results?

Create a new vector index 'image-features' with 512 dimensions

Index created! 'image-features' is now initialized with 512 dimensions and is ready for document ingestion. I can now help you index your first embedding document.

List all vector indexes in my cluster

Retrieving indexes... I found 3 vector-enabled indexes: 'product-embeddings' (1536 dims), 'image-features' (512 dims), and 'text-semantic-v1' (768 dims). Which one would you like to inspect?

Yes. Use the 'search' tool. Provide the index name and a JSON array representing your query vector. The agent will perform raw K-Nearest Neighbors computations to find the most semantically similar documents.

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