Elastic Enterprise Search MCP. Manage search indexes and run deep queries from your agent.
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…and any MCP-compatible client
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Elastic Enterprise Search connects your AI client to your company's search engines. It lets you manage document indexing, run complex searches, and audit search performance across structured data sources.
You can run these operations—from listing engines to analyzing click logs—using natural conversation with your agent.
What your AI agents can do
Analytics
Gets and calculates search performance metrics and usage insights.
Get engine
Retrieves the detailed configuration and status of a specific search engine.
Index documents
Sends newly created JSON data to store and update documents in specific schemas.
Run list_engines to see all configured search engines in your deployment.
Execute search to find documents within a specified engine using natural language queries.
Use index_documents to send new JSON payloads and store them into defined schemas.
Call list_documents to get a list of documents already stored in an engine.
Generate search analytics using analytics to monitor usage and calculate click logs.
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Supported MCP Clients
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Elastic Enterprise Search MCP Server: 6 Tools for Search and Data
Use these tools to discover, index, search, and audit documents across all your configured search engines.
019d758eanalytics
Gets and calculates search performance metrics and usage insights.
019d758eget engine
Retrieves the detailed configuration and status of a specific search engine.
019d758eindex documents
Sends newly created JSON data to store and update documents in specific schemas.
019d758elist documents
Retrieves a list of document identifiers and metadata from an engine.
019d758elist engines
Shows all available search engines configured in the deployment.
019d758esearch
Runs a semantic or literal query to find documents within a designated engine.
Choose How to Get Started
Build a custom MCP for your own tools, or connect a ready-made integration from our catalog.
Build Your Own
Turn any API into an MCP. Import a spec, define Agent Skills, or deploy with MCPFusion.
- Import from OpenAPI, Swagger, or YAML specs
- Create Agent Skills with progressive disclosure
- Deploy to edge with MCPFusion framework
- Built in DLP, auth, and compliance on every call
- Real time usage dashboard and cost metering
- Publish to catalog or keep private
Make Your AI Do More
Start with Elastic Enterprise Search, then connect any of our 4,700+ other servers whenever your AI needs more. One click, no limits.
- Use this MCP plus 4,700+ others, all in one place
- Add new capabilities to your AI anytime you want
- Every connection is secured and compliant automatically
- Track usage and costs across all your servers
- Works with Claude, ChatGPT, Cursor, and more
- New servers added to the catalog every week
What you can do with this MCP connector
Yo, this thing hooks up your AI client to your whole company's search engines. You get full control over how you manage documents and what your people actually find using natural talk with your agent. You can check all your search engines with list_engines. To find documents, you just run search, letting your agent use natural language queries to find stuff in a specific engine.
Need to upload new data? You use index_documents to send new JSON payloads and store 'em in defined schemas. If you wanna see what documents are already stored, you call list_documents to get a list of document identifiers and metadata. Wanna check how the search is doing? You run analytics to generate search analytics, letting you monitor usage and calculate click logs.
You can also grab the detailed setup and status of any single search engine by running get_engine.
How Elastic Enterprise Search MCP Works
- 1 Subscribe to the server, then provide your Elastic Enterprise Search URL and API Key (find the key in Kibana > Stack Management > Security > API Keys).
- 2 Your AI client sends a request, like 'Search for X in Y engine.' The server validates the request and executes the necessary tool calls.
- 3 The agent receives the structured data (e.g., search results, analytics metrics) and presents it back to you in natural language.
The bottom line is you manage your search engines and data lifecycle entirely through your chat interface, without writing API calls.
Who Is Elastic Enterprise Search MCP For?
This is for Search Engineers who need to test relevance without manual API calls. It's for Developers who need to index documents or check search results directly from their IDE. Data Analysts use it to audit search metrics. Ops Teams use it to verify engine health and manage document pipelines in real-time.
Tests search relevance and monitors engine configurations without writing API calls or running manual tests.
Indexes new documents or verifies search results directly from the IDE or chat window.
Audits search analytics and monitors click logs by asking natural language questions about usage.
Verifies engine health and manages document indexing pipelines to ensure continuous service operation.
What Changes When You Connect
- See engine configurations and list all available search spaces using
list_enginesandget_engine. You don't need to jump through the Kibana UI to verify engine status. - Check search relevance quickly. Use the
searchtool to run deep, contextual queries against structured enterprise scopes without writing complex query DSL. - Keep your data current.
index_documentshandles bulk payload ingestions, triggering pipelines to store and update collections synchronously. - Audit usage patterns by calling
analytics. This gives you precise internal metrics and exact click log data, helping you justify resource spend. - Verify document status. Use
list_documentsto confirm which physical raw records are indexed, andlist_enginesto see the scope. - Retrieve raw data directly. The
list_documentstool lets you extract attached REST arrays, fetching physical records flawlessly for deep inspection.
Real-World Use Cases
A dev needs to check if a new data source is indexed.
The developer runs list_engines to confirm the correct engine name. Then, they call list_documents on that engine to see if the document ID exists. If it's missing, they use index_documents to push the JSON payload. The problem is solved in three simple steps.
The marketing team needs to see what people are searching for.
The analyst asks the agent to 'Show me last month's search volume for shoes.' The agent runs analytics to pull the usage insights. It returns the top queries and the click-through rate, allowing the marketing team to adjust product visibility immediately.
An Ops team needs to validate engine health after a migration.
Instead of logging into the dashboard, the Ops engineer uses the agent to run get_engine for the primary catalog engine. This verifies the index layout and configuration nodes are intact, confirming the migration succeeded before the business starts using it.
A user needs to find a specific document deep in the corporate knowledge base.
The user asks, 'Find the policy on remote work.' The agent uses the search tool against the 'HR-docs' engine. It returns the top result, including a score and snippet, allowing the user to get the answer without manual keyword tweaking.
The Tradeoffs
Treating it like a single search bar
Just running search hoping it finds everything. You might get great results, but you'll miss the context—you won't know if the documents are even indexed or if the engine is configured correctly.
→
Always start with discovery. Use list_engines to identify the correct scope, then use get_engine to confirm its status. Only then should you run search.
Indexing data without checking the schema
Running index_documents with a payload that doesn't match the expected schema. This fails silently or, worse, corrupts the index structure, making subsequent searches inaccurate.
→
Before indexing, use the agent to check the metadata using list_documents or get_engine to verify the required schema structure. This prevents data corruption.
Ignoring usage trends
Just building the search function and forgetting to monitor it. You don't know if people are searching for what you think they are, leading to poor product experience.
→
After deployment, run analytics regularly. Monitor the click log data and usage insights. This tells you where the real user pain points are.
When It Fits, When It Doesn't
Use this server if your goal is structured data discovery, deep document retrieval, or quantifiable performance auditing. It's perfect for Search Engineers and Data Analysts who need to prove why a search result is relevant. Don't use it if you just need a quick, one-off keyword search on a simple website. For basic keyword searching, a standard website search widget is fine. This tool manages the entire lifecycle: listing engines (list_engines), checking health (get_engine), loading data (index_documents), and analyzing performance (analytics).
Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Elastic Enterprise Search. 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.
Available Capabilities
Manually checking search relevance is a huge time sink.
Right now, if you need to validate a search feature, you have to jump between the Kibana dashboard, run manual queries in the console, and then copy-paste the results into a spreadsheet. If you change a field or the index structure, you run the whole cycle again, wasting hours just confirming the data is accessible.
With this MCP server, you just ask your agent: 'Show me the structure of the 'product catalog' engine.' The agent runs `get_engine`, pulls the index layout, and gives you the configuration nodes right in your chat. It's instant, and it's actionable.
Elastic Enterprise Search MCP Server: Deep Search and Indexing
You no longer have to write complex API calls to check document existence or manually upload JSON payloads. The agent handles the logic. You simply tell it, 'Index this new product data.' The tool runs `index_documents` and confirms the payload was processed.
This shifts your focus from API plumbing to problem-solving. You get the answer instantly, confirmed by the tool execution, without leaving your primary workflow.
Common Questions About Elastic Enterprise Search MCP
How do I use the `list_engines` tool? +
You ask the agent to list all search engines. It runs list_engines and returns a list of every engine name in your deployment. This helps you know which scope to work with next.
What does the `search` tool require? +
The search tool needs the document query and the specific engine name. You tell the agent both, and it runs the search, returning relevant documents and their scores.
How do I check analytics using the `analytics` tool? +
Just ask the agent to 'Show me search analytics for [engine name].' The tool runs, and you get metrics like total queries and click-through rate for that engine.
Can I use `index_documents` to fix bad data? +
Yes. index_documents allows you to send new JSON documents targeting a specific schema. If the data is corrected, you send it through this tool to update the record.
What do I need to know before using the `list_engines` tool? +
You must first provide a valid Elastic Enterprise Search URL and an API Key. The API Key is found in Kibana under Stack Management > Security > API Keys. This ensures your agent has the necessary credentials to connect to your specific deployment.
How do I ensure the data I pass to `index_documents` matches my schema? +
The index_documents tool requires newly created JSON documents and targets specific schemas. If your data doesn't match, the operation will fail. Always validate your JSON payloads against the target index mapping before calling the tool.
What is the difference between `list_documents` and `search`? +
list_documents retrieves a list of physical raw records from an engine. The search tool executes a query against the engine's content, returning the most relevant matches based on your search terms.
Does the `analytics` tool support time range filtering? +
Yes, the analytics tool tracks usage insights and calculates click log data. You specify the desired time window (e.g., 'last 7 days') in the tool's parameters to get a focused performance report.
Can my agent list all available search engines in Elastic? +
Yes. Use the 'list_engines' tool. The agent iterates through your engine containers, managing logical indexing schemas and providing a complete map of your search spaces.
How do I index a batch of JSON documents via chat? +
Use the 'index_documents' tool. Provide the engine name and a JSON array of your documents. The agent will command the bulk payload ingestion, triggering native pipeline mappings to store your data synchronously.
Can I check the search analytics for a specific engine through the agent? +
Absolutely. The 'analytics' tool generates precise internal metric tracking for your engine. It will isolate usage insights and calculate click log data, allowing you to monitor search performance natively.
Use it with your favorite AI tools
Connect this server to Cursor, Claude, VS Code, and more.
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