Can my agent perform kNN searches using raw vector arrays?

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.

How do I create a new vector index with specific dimensions via chat?

Use the 'create_index' tool. You can specify the index name and the number of dimensions (e.g., 1536 for OpenAI embeddings). The agent will provision the strictly typed data structure in your Elasticsearch cluster.

Can I delete a single document from a vector index through the agent?

Absolutely. Use the 'delete_document' tool with the index and document ID. The agent will enforce immediate document vaporization, stripping the record from the physical Lucene partitions.

Elasticsearch Vector MCP Server

Name: Elasticsearch Vector
Availability: InStock
Author: Vinkius

elastic.co/elasticsearch/vector-search

Built by Vinkius GDPR ToolsGratis

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

Vinkius AI Gateway soporta streamable HTTP y SSE.

Funciona con todos los agentes de IA que ya usas

…y cualquier cliente compatible con MCP

Elasticsearch Vector MCP Server: mira tu AI Agent en acción

AI Agent→Vinkius→Elasticsearch Vector

You

Vinkius AI Gateway

GDPR·High Security·Kill Switch·Ultra-Low Latency·Plug and Play

Capacidades integradas (6)

create_index

Create dense_vector index

delete_document

Delete a document

get_index

Get index info

index_document

Index a document

list_indexes

List all indexes

Dense vector knn search

Lo que este conector desbloquea

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

Preguntas frecuentes

Dale a tus agentes de IA el poder de Elasticsearch Vector

Accede a Elasticsearch Vector y a más de 2.000 servidores MCP — listos para que tus agentes los usen, ahora mismo. Sin código pegamento. Sin integraciones personalizadas. Solo conecta el Vinkius AI Gateway y deja que tus agentes trabajen.

DETALLES

CategoríaBrain Trust

Etiquetas

vector-search knn-search embeddings semantic-search indexing ai-infrastructure

Support 24/7

support@vinkius.com

Security

Vinkius Trust Center

SLA

Service Level Agreement

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