# Elastic Enterprise Search MCP for AI Agents MCP

> Elastic Enterprise Search MCP connects your AI agents directly to robust enterprise search engines. It lets you manage complex document indexes, run deep contextual queries across multiple data scopes, and audit usage metrics—all through natural conversation. Stop writing boilerplate API calls; start asking questions about your company's knowledge base.

## Overview
- **Category:** industry-titans
- **Price:** Free
- **Tags:** enterprise-search, indexing, semantic-search, workplace-discovery, data-retrieval, search-analytics

## Description

Need to find something buried deep in your corporate documents? This MCP gives your AI agent full control over your entire search environment. Instead of manually checking Kibana dashboards or running complex REST queries, you simply ask your agent what you need. It manages the connections and executes everything from listing available search engines to performing detailed searches on specific document collections.

The system handles bulk data ingestion for updating indexes and provides precise analytics tracking, so you know exactly which parts of your knowledge base are popular and which ones are failing. When you subscribe through Vinkius, your agent gains access to this powerful capability alongside thousands of others, letting you manage everything from search queries to document lifecycle status without ever leaving your chat interface.

## Tools

### list_engines
Retrieves a list of all deployed search engines managed by the system.

### get_engine
Fetches detailed configuration and status information for a single engine.

### search
Finds specific documents across an engine using natural language queries.

### list_documents
Displays the metadata and basic details of all indexed content in a given search engine.

### index_documents
Accepts new JSON data payloads to update or add documents to specific schemas.

### analytics
Calculates and retrieves detailed search usage metrics and performance reports.

## Prompt Examples

**Prompt:** 
```
What were our top search topics last month?
```

**Response:** 
```
Here’s a breakdown of your activity over the past 30 days:

| Metric | Value |
| :--- | :--- |
| Total Queries | 8,520 |
| Avg. CTR | 7.1% |
| Top Query | 'SaaS integration costs' (450 queries) |

We see a big spike in questions about billing structure; maybe we need to update the pricing guide.
```

**Prompt:** 
```
Show me the status of our product documentation engine.
```

**Response:** 
```
The 'product-docs' engine is currently: **Operational**.

*   **Document Count:** 12,450 records
*   **Last Indexed:** 2 hours ago (Success)
*   **Schema Version:** v3.1

The configuration looks good. Everything seems healthy.
```

**Prompt:** 
```
Can you find any articles about migrating to the new cloud platform?
```

**Response:** 
```
**Searching 'help-center-docs' for 'cloud migration'...**

I found 4 relevant documents:
*   *Cloud Migration Guide v2.0* (Score: 18.9) - Full text available.
*   *Migration Checklist* (Score: 15.2) - Metadata only.
*   *API Endpoint Changes* (Score: 12.5) - Partial snippet found.
```

## Capabilities

### List all deployed search engines
Retrieves a list of every available search engine and its current operational status.

### Get detailed information on an engine
Fetches comprehensive metadata and configuration details for a single, specified search engine.

### Search documents within an engine
Runs natural language queries across the content of one or more engines to find relevant document snippets.

### List indexed documents metadata
Retrieves a list and basic details about all documents currently stored in a designated search engine.

### Ingest new JSON documents
Sends bulk payloads of newly created data to be processed and stored within specific schemas.

### Generate usage analytics reports
Calculates usage insights, tracks click logs, and generates performance metrics for search activity.

## Use Cases

### A team needs to audit product search performance
Instead of running weekly reports on click-through rates, an analyst asks their agent: 'Show me the search analytics for e-commerce products.' The agent executes the appropriate tool and delivers a summary showing top queries and overall traffic trends.

### A developer needs to onboard new documents
A developer has finished writing 100 new internal guides. They instruct their agent: 'Index these 100 JSON files into the help center.' The agent uses the indexing tool, validating that all data is stored correctly and synchronously.

### The operations team needs to verify system health
An ops engineer wants to know if a specific content area is even indexed. They ask their agent: 'List documents for the marketing engine.' The agent replies with a clean list of metadata, confirming the scope and size of the index.

### A user needs to find information across departments
A manager asks: 'What's the policy on remote work?' The agent doesn't just search one place; it uses the core search capability to check multiple connected engines simultaneously, providing a comprehensive answer from various sources.

## Benefits

- Stop debugging search relevance manually. Your agent can test query performance across multiple engines using the 'get engine' capability, letting you confirm system health instantly.
- Update your company knowledge base easily. Use the 'index documents' tool to bulk upload new JSON data without touching a pipeline or writing code.
- Pinpoint content gaps instantly. The 'analytics' function allows you to audit search usage and see exact click logs, telling you precisely what information employees are looking for but can’t find.
- Consolidate discovery. You don't need dozens of dashboards; your agent can list all engines and run targeted searches across them using the core 'search' capability.
- Deepen data visibility. The MCP allows you to inspect index layouts and metadata by calling the engine details tool, giving you a clean view of the underlying data structure.

## How It Works

The bottom line is that you talk naturally, and the MCP handles all the complex back-end API calls needed for enterprise search management.

1. Subscribe to the Elastic Enterprise Search MCP on Vinkius.
2. Provide your specific Elastic Enterprise Search URL and API Key (you can find this key in Kibana's Stack Management).
3. Use your AI client to ask questions. Your agent will interpret your request, execute the necessary search or indexing operations, and return a clean summary of the results.

## Frequently Asked Questions

**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.