Bring Vector Search
to LlamaIndex
Create your Vinkius account to connect Elasticsearch Vector to LlamaIndex and start using all 6 AI tools in minutes. Fully managed, enterprise secure, and ready to use without writing a single line of code. No hosting, no server setup — just connect and start using.
GDPR Free for SubscribersCompatible with every major AI agent and IDE
Gemini
What is the Elasticsearch Vector MCP Server?
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_vectorembedding 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
- Subscribe to this server
- Enter your Elasticsearch Host URL and API Key (found in Kibana > Stack Management > Security > API Keys)
- 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
Built-in capabilities (6)
Create dense_vector index
Delete a document
Get index info
Index a document
List all indexes
Dense vector knn search
Why LlamaIndex?
LlamaIndex agents combine Elasticsearch Vector tool responses with indexed documents for comprehensive, grounded answers. Connect 6 tools through Vinkius and query live data alongside vector stores and SQL databases in a single turn. ideal for hybrid search, data enrichment, and analytical workflows.
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Data-first architecture: LlamaIndex agents combine Elasticsearch Vector tool responses with indexed documents for comprehensive, grounded answers
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Query pipeline framework lets you chain Elasticsearch Vector tool calls with transformations, filters, and re-rankers in a typed pipeline
- —
Multi-source reasoning: agents can query Elasticsearch Vector, a vector store, and a SQL database in a single turn and synthesize results
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Observability integrations show exactly what Elasticsearch Vector tools were called, what data was returned, and how it influenced the final answer
Elasticsearch Vector in LlamaIndex
Why run Elasticsearch Vector with Vinkius?
The Elasticsearch Vector connection runs on our fully managed, secure cloud infrastructure. We handle the hosting, maintenance, and security so you don't have to deal with servers or code. All 6 tools are ready to work instantly without any complex setup.
You stay in complete control of your data. Your AI only accesses the information you approve, keeping your sensitive passwords and private details completely safe. Plus, with automatic optimizations, your AI works faster and more efficiently.
* Every connection is hosted and maintained by Vinkius. We handle the security, updates, and infrastructure so you don't have to write code or manage servers. See our infrastructure
Over 4,000 integrations ready for AI agents
Explore a vast library of pre-built integrations, optimized and ready to deploy.
Connect securely in under 30 seconds
Generate tokens to authenticate and link external services in a single step.
Complete visibility into every agent action
Audit live requests, latency, success rates, and active security compliance policies.
Optimize spending and track token ROI
Analyze real-time token consumption and cost metrics detailed by connection.
Explore our live AI Agents Analytics dashboard to see it all working
This dashboard is included when you connect Elasticsearch Vector using Vinkius. You will never be left in the dark about what your AI agents are doing with your tools.
Elasticsearch Vector and 4,000+ other AI tools. No hosting, no code, ready to use.
Professionals who connect Elasticsearch Vector to LlamaIndex through Vinkius don't need to write code, manage servers, or worry about security. Everything is pre-configured, secure, and runs automatically in the background.
Raw MCP | Vinkius | |
|---|---|---|
| Ready-to-use MCPs | Find and configure each manually | 4,000+ MCPs ready to use |
| Connection Setup | Manual coding & server setup | 1-click instant connection |
| Server Hosting | You host it yourself (needs 24/7 uptime) | 100% hosted & managed by Vinkius |
| Security & Privacy | Stored in plaintext config files | Bank-grade encrypted vault |
| Activity Visibility | Blind execution (no logs or tracking) | Live dashboard with real-time logs |
| Cost Control | Runaway AI token spend risk | Automatic budget limits |
| Revoking Access | Must delete files or code to stop | 1-click disconnect button |
How Vinkius secures
Elasticsearch Vector for LlamaIndex
Every request between LlamaIndex and Elasticsearch Vector is protected by our secure gateway. We automatically keep your sensitive data private, prevent unauthorized access, and let you disconnect instantly at any time.
Frequently asked questions
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.
How does LlamaIndex connect to MCP servers?
Use the MCP client adapter to create a connection. LlamaIndex discovers all tools and wraps them as query engine tools compatible with any LlamaIndex agent.
Can I combine MCP tools with vector stores?
Yes. LlamaIndex agents can query Elasticsearch Vector tools and vector store indexes in the same turn, combining real-time and embedded data for grounded responses.
Does LlamaIndex support async MCP calls?
Yes. LlamaIndex's async agent framework supports concurrent MCP tool calls for high-throughput data processing pipelines.
BasicMCPClient not found
Install: pip install llama-index-tools-mcp
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