Elasticsearch Vector MCP. Perform semantic searches using vectors.
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Elasticsearch Vector gives your agent full control over semantic discovery and vector search within Elasticsearch. You can perform raw K-Nearest Neighbors (kNN) computations against multi-dimensional embedding arrays, manage complex index mappings, and ingest large volumes of embedding documents directly from any AI client.
What your AI agents can do
Create index
Builds a new index specifically for storing dense vector data.
Delete document
Removes a specific document from an index using its unique ID.
Get index
Retrieves detailed information and mappings for a single, specified index.
Find documents that are semantically closest to a given vector by performing raw K-Nearest Neighbors calculations.
Create, list, and check the metadata of specific indices designed to store high-dimensional embedding vectors.
Bulk insert new data by attaching exact dense vector payloads into the physical Lucene partitions.
Permanently delete documents from indexes using specific UUID identifiers.
Retrieve detailed mapping rules and dimensional constraints for an index to ensure data readiness.
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Supported MCP Clients
OAuth 2.0 Compatible Gemini Waiting for input…
Elasticsearch Vector: 6 Available Tools
Use these tools to create, read, update, delete, and search complex vector data stores directly through your agent.
Make your AI actually useful.
Add this MCP to Claude, Cursor, or Windsurf and your AI stops guessing. It gets real tools to look things up, take action, and handle the stuff you keep doing by hand.
Start using Elasticsearch Vector on Vinkius019d758ecreate index
Builds a new index specifically for storing dense vector data.
019d758edelete document
Removes a specific document from an index using its unique ID.
019d758eget index
Retrieves detailed information and mappings for a single, specified index.
019d758eindex document
Adds or updates an existing document by attaching its embedding vector to the index.
019d758elist indexes
Lists every available index within the cluster, helping you see what data stores are active.
019d758esearch
Performs a dense vector kNN search to find documents most similar to your input vector.
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Build Your Own
Turn any API into an MCP. Import a spec, define Agent Skills, or deploy with MCPFusion.
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Start with Elasticsearch Vector, then connect any of our 4,900+ other servers whenever your AI needs more. One click, no limits.
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Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Elasticsearch Vector. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.
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Works with Claude, ChatGPT, Cursor, and more
The Model Context Protocol standardizes how applications expose capabilities to LLMs. Instead of operating in isolation, your AI gains direct access to external platforms, live data, and real-world actions through secure, standardized connections.
This server provides 6 capabilities that interface natively with Claude, ChatGPT, Cursor, and any MCP client. No middleware. No custom integration required.
The struggle of searching complex documents today
Currently, when an engineer needs to find related information across a massive knowledge base, they are usually limited to simple search fields. They have to copy the query into one dashboard, check another for index status, and then manually run separate scripts to compare results against other data streams.
With this MCP, your agent handles all that complexity automatically. You ask it a question—'What documents relate to X?'—and it uses vector math to surface the most contextually similar records directly, giving you a complete answer without switching tools or tabs.
Control Indexing and Search with Elasticsearch Vector
The pain points of managing indices—like ensuring the correct dimension count or knowing which documents are actually available for searching—are gone. You no longer have to guess if an index is ready; you can run `get_index` to check its schema, and use `list_indexes` for a definitive inventory.
What's different now is that data lifecycle management becomes conversational. Your agent doesn't just search; it manages the infrastructure required *to* search.
What you can do with this MCP connector
This MCP connects your entire workflow to an Elasticsearch cluster, giving you deep control over vector search and semantic data. Instead of relying on basic keyword matching, you can map absolute semantic similarity across huge datasets using dense vector embeddings. The system allows you to manage the underlying structure—creating new indices, checking mappings, and even cleaning up old records by UUID.
When your agent needs to find contextually related information from raw unstructured text or images, it handles those complex calculations for you. This makes the process of turning data into actionable knowledge much more direct. You can connect this power through Vinkius, giving any MCP-compatible client immediate access to sophisticated vector capabilities.
019d758e-c4fd-70d4-96e1-46dd2e3e7e1d 
How Elasticsearch Vector MCP Works
- 1 Subscribe to this MCP, then input your Elasticsearch Host URL and API Key. These credentials control the connection.
- 2 Your agent sends a command—for example, 'Search for similar items in index X.' The tool handles the complex vector math.
- 3 The system returns a list of results, including similarity scores and metadata, that your agent uses to continue the conversation.
The bottom line is you get programmatic access to industrial-strength vector search without writing a single query language command.
Who Is Elasticsearch Vector MCP For?
This MCP targets data professionals who deal with massive, complex datasets where simple keyword matching fails. Think of the ML Engineer debugging an embedding model or the Data Scientist verifying index performance in real time.
Tests and validates new embedding models by running raw kNN searches against stored vectors, ensuring semantic drift hasn't corrupted the data.
Monitors index mappings and dimensional constraints on a cluster to ensure that newly generated vector features are compatible with existing search indexes.
Manages the life cycle of multiple vector indices, knowing when to run create_index versus when to simply update content using index_document.
What Changes When You Connect
- Find out exactly what's wrong with your search setup. Using the
get_indextool lets you analyze dimensional constraints and index mappings before running a query. - Stop wasting time on manual data cleanup. The
delete_documenttool allows you to instantly vaporize records from physical indices by providing exact UUIDs. - Quickly see all available storage namespaces in your cluster. Running
list_indexesgives you an instant inventory of every vector-enabled index. - Ingest new knowledge without writing bulk scripts. The
index_documenttool lets your agent attach a dense vector payload to persist data instantly. - Perform deep similarity checks with the
searchtool, running raw kNN computations that go far beyond simple keyword matches.
Real-World Use Cases
Retrieving context for an LLM agent
An ML engineer needs to ground a new chatbot on proprietary documents. The agent runs list_indexes to find the correct corpus, then uses search with a query vector to pull only the most relevant text snippets, avoiding irrelevant noise.
Updating product metadata
A software developer uploads new product images and embeddings. They run create_index for the image features, then use index_document repeatedly to ensure all old records are correctly updated with the new vector data.
Auditing sensitive data
An Ops Team member needs to remove a user's profile entirely. They use get_index first to confirm the schema, then call delete_document using the target UUID to ensure complete and irreversible removal.
Testing new embedding models
A Data Scientist wants to test a brand-new vector model. They use create_index for the test data, then run multiple search calls with different vectors to compare performance against the existing production index.
The Tradeoffs
Assuming all indexes are ready
Running a kNN search immediately after creating an index without verifying its schema. The agent will fail because the dimensional constraints aren't met.
→
Always call get_index first to inspect the mapping rules and confirm that the dimensions match your embedding payload before running any search or index_document calls.
Manually guessing index names
Trying to run a search query on an index name that was recently renamed or deleted. The process stalls because the system can't find the target data store.
→
Start by calling list_indexes. This gives you a real-time map of every active storage namespace, ensuring you use the correct name for all future calls.
Overwriting critical records
Running bulk inserts (index_document) without first checking if the index structure is still valid or if old data needs to be kept.
→
Verify your current schema using get_index. If you suspect a structural issue, consider running create_index for a clean slate instead of blindly overwriting.
When It Fits, When It Doesn't
Use this MCP if your core problem is semantic understanding: finding data based on meaning rather than keywords. This is essential when dealing with high-dimensional embedding vectors. Don't use it if you only need simple key/value lookups or basic relational database queries; those require a different type of tool. If you are managing structured, non-vectorized content, stick to standard CRUD operations in your existing system. However, if you have generated embeddings and need to perform kNN searches, this is the authoritative source for controlling that data lifecycle.
Common Questions About Elasticsearch Vector MCP
How does the `search` tool perform vector lookups? +
The search tool executes raw kNN computations against your specified index. It takes a dense vector as input and returns documents with high similarity scores, helping you find semantically related data.
What is the difference between `index_document` and `create_index`? +
create_index builds the empty container—the index itself. You must run this first. Then, you use index_document to fill that container by adding actual embedding payloads.
I need to remove data; should I use `delete_document`? +
Yes, if you know the exact UUID of the document you want gone, delete_document performs an immediate and hard removal from the index. This is irreversible.
How do I check which indexes are available for search? +
You call list_indexes. This provides a complete list of all vector-enabled storage namespaces currently managed by your cluster, letting you know what data sources exist.
When calling `get_index`, how can I verify the dimensional constraints and schema rules for my vector data? +
The tool reports the index's mapping structure. It lists specific required dimensions, ensuring your embeddings adhere to the exact numeric format before you try to write them.
If I need to process hundreds of documents, is there an efficient way to use `index_document`? +
Yes, while index_document handles single writes, sending data in bulk operations significantly boosts performance. This method allows you to attach many embedding payloads synchronously.
What authentication credentials do I need when using tools like `list_indexes`? +
You must provide your Elasticsearch Host URL and a valid API Key. These keys are generated within Kibana under the Stack Management security settings, giving your agent read access.
If I attempt to use `delete_document` with an incorrect UUID, what does that mean for my workflow? +
The operation will fail gracefully and return a specific error status. This allows your AI client to catch the failure immediately and continue processing without interruption or system crash.
Use it with your favorite AI tools
Connect this server to Cursor, Claude, VS Code, and more.