Elasticsearch Vector MCP, Ready to Go
Connect your Elasticsearch cluster to Claude or Cursor with the Elasticsearch Vector MCP to manage vector search and embeddings via your AI agent.
No credit card required. Experience the power of this integration risk-free.
Manage your vector search and semantic discovery workflows with natural language commands.
Works with every AI agent you already use
…and any MCP-compatible client
How fast is the Elasticsearch Vector Connector?
Average time for the server to become ready for requests over the last 14 days, measured until the initialize / tools/list handshake completes. Metrics are updated daily between 00:00 and 04:00 UTC. Create a free account, use this Connector on Vinkius Cloud, and connect it to your AI agent in seconds.
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What AI agents can do with Elasticsearch Vector 6 Tools for Vector Search
Use these tools to search, index, and manage your Elasticsearch vector data through your AI agent.
Search
Run a dense vector kNN search to find semantically similar documents. This helps you find relevant results based on vector similarity.
List indexes
See every active index in your Elasticsearch cluster. This provides a quick overview of your current storage namespaces.
Get index
Look up the specific mapping and dimension details for a single index. Use this to verify your configuration before indexing.
Index document
Add a new document with its dense_vector embedding to your storage. This handles the insertion into your Lucene partitions.
Delete document
Remove a specific record from your physical index using its UUID. This is the fastest way to invalidate specific data.
Create index
Set up a new dense_vector index with the correct number of dimensions. This allows you to provision new search structures quickly.
A Connector is a URL. Vinkius runs it: hosting, security, governance, observability.
You're looking at one of 5,800+ managed Connectors. The real value isn't the catalog. It's the control plane that secures, governs, audits, and manages every interaction between your agents and the tools they use.
No Shadow AI
Every agent action is visible, approved, and auditable. Nothing runs outside your governance.
Absolute agent control
Fine-grained permissions for every agent, MCP, and tool. Instantly revoke access and audit every execution.
Cost control per token
Spend broken down to the token, tool, and agent. Budgets and hard limits. No surprise invoices.
Managed & monitored infra
We operate the runtime, authentication, scaling, retries, and monitoring. Your team manages AI, not infrastructure.
Data protection, DLP by design
Sensitive data is filtered before reaching the model. Access is governed so agents receive only the information they're allowed to use.
Token optimization, real savings
Lower AI costs by delivering the right context instead of unnecessary tools. Better accuracy, faster responses, and fewer wasted tokens.
Elasticsearch Vector MCP for Semantic Search Management
This is for the engineers and data scientists who are tired of writing boilerplate query code just to see if their embeddings actually work. It's for the people who need to manage production vector data without leaving their chat interface.
AI Engineer
Testing new embedding models and verifying kNN results without writing complex DSL.
Software Developer
Indexing documents and checking search similarity directly from the IDE or chat.
Data Scientist
Monitoring index mappings and dimensional constraints using natural language.
Ops Engineer
Verifying cluster health and managing vector namespaces in real-time.
Frequently Asked Questions
Can the Elasticsearch Vector MCP help me manage my embeddings? +
Yes, it lets your agent handle the indexing and searching of your dense vector data directly, making it easy to manage your embeddings without manual scripts.
How do I connect my Elasticsearch cluster to my AI agent? +
You just need your Host URL and an API Key from your Kibana security settings to link the Connector to your AI client.
Does this Connector support kNN searches? +
It specifically handles dense vector kNN computations to find the most similar items in your data based on semantic similarity.
Can I use this to delete specific records? +
You can use the delete tool to remove documents from your physical indices using their unique UUIDs, which is great for data cleanup.
Is this good for checking my index mappings? +
Yes, it allows your agent to pull and display the specific rules and dimensions for any index in your cluster to ensure your configuration is correct.
Can my agent create new vector indexes for me? +
It can provision new dense_vector structures with the exact dimensions you specify in plain English, saving you from manual configuration.
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.
Your AI, connected to everything.
No credit card required · Free tier available
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