Elastic Enterprise Search MCP Server for LlamaIndex 6 tools — connect in under 2 minutes
LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add Elastic Enterprise Search as an MCP tool provider through the Vinkius and your agents can query, analyze, and act on live data alongside your existing indexes.
ASK AI ABOUT THIS MCP SERVER
Vinkius supports streamable HTTP and SSE.
import asyncio
from llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI
async def main():
# Your Vinkius token — get it at cloud.vinkius.com
mcp_client = BasicMCPClient("https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")
mcp_tool_spec = McpToolSpec(client=mcp_client)
tools = await mcp_tool_spec.to_tool_list_async()
agent = FunctionAgent(
tools=tools,
llm=OpenAI(model="gpt-4o"),
system_prompt=(
"You are an assistant with access to Elastic Enterprise Search. "
"You have 6 tools available."
),
)
response = await agent.run(
"What tools are available in Elastic Enterprise Search?"
)
print(response)
asyncio.run(main())
* Every MCP server runs on Vinkius-managed infrastructure inside AWS - a purpose-built runtime with per-request V8 isolates, Ed25519 signed audit chains, and sub-40ms cold starts optimized for native MCP execution. See our infrastructure
About Elastic Enterprise Search MCP Server
Connect your Elastic Enterprise Search deployment to any AI agent and take full control of your application search engines and workplace discovery through natural conversation.
LlamaIndex agents combine Elastic Enterprise Search tool responses with indexed documents for comprehensive, grounded answers. Connect 6 tools through the Vinkius and query live data alongside vector stores and SQL databases in a single turn — ideal for hybrid search, data enrichment, and analytical workflows.
What you can do
- Engine Orchestration — Iterate through explicit engine containers managing logical indexing schemas and search spaces completely
- Search & Discovery — Resolve semantic or literal queries enforcing deep contextual matches against structured enterprise scopes seamlessly
- Document Indexing — Command explicit bulk payload ingestions triggering native pipeline mappings to store and update document collections synchronously
- Metadata Inspection — Analyze specific global bounds fetching discrete index layouts and extracting linguistic configuration nodes cleanly
- Analytics Auditing — Generate precise internal metric tracking isolating usage insights and calculating exact click log data to monitor performance
- Catalog Retrieval — Extract explicitly attached REST arrays mapping exact document payloads fetching physical raw records flawlessly
The Elastic Enterprise Search MCP Server exposes 6 tools through the Vinkius. Connect it to LlamaIndex in under two minutes — no API keys to rotate, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.
How to Connect Elastic Enterprise Search to LlamaIndex via MCP
Follow these steps to integrate the Elastic Enterprise Search MCP Server with LlamaIndex.
Install dependencies
Run pip install llama-index-tools-mcp llama-index-llms-openai
Replace the token
Replace [YOUR_TOKEN_HERE] with your Vinkius token
Run the agent
Save to agent.py and run: python agent.py
Explore tools
The agent discovers 6 tools from Elastic Enterprise Search
Why Use LlamaIndex with the Elastic Enterprise Search MCP Server
LlamaIndex provides unique advantages when paired with Elastic Enterprise Search through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine Elastic Enterprise Search tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain Elastic Enterprise Search tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query Elastic Enterprise Search, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what Elastic Enterprise Search tools were called, what data was returned, and how it influenced the final answer
Elastic Enterprise Search + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the Elastic Enterprise Search MCP Server delivers measurable value.
Hybrid search: combine Elastic Enterprise Search real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query Elastic Enterprise Search to augment indexed data with live information before generating user-facing responses
Knowledge base agents: build agents that maintain and update knowledge bases by periodically querying Elastic Enterprise Search for fresh data
Analytical workflows: chain Elastic Enterprise Search queries with LlamaIndex's data connectors to build multi-source analytical reports
Elastic Enterprise Search MCP Tools for LlamaIndex (6)
These 6 tools become available when you connect Elastic Enterprise Search to LlamaIndex via MCP:
analytics
Get search analytics
get_engine
Get engine
index_documents
Index newly created JSON documents targeting specific schemas
list_documents
List indexed documents in an engine
list_engines
List engines
search
Search documents within an engine
Example Prompts for Elastic Enterprise Search in LlamaIndex
Ready-to-use prompts you can give your LlamaIndex agent to start working with Elastic Enterprise Search immediately.
"List all search engines in my Elastic deployment"
"Search for 'api integration' in engine 'help-center-docs'"
"Show me search analytics for engine 'e-commerce-products'"
Troubleshooting Elastic Enterprise Search MCP Server with LlamaIndex
Common issues when connecting Elastic Enterprise Search to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpElastic Enterprise Search + LlamaIndex FAQ
Common questions about integrating Elastic Enterprise Search MCP Server with LlamaIndex.
How does LlamaIndex connect to MCP servers?
Can I combine MCP tools with vector stores?
Does LlamaIndex support async MCP calls?
Connect Elastic Enterprise Search with your favorite client
Step-by-step setup guides for every MCP-compatible client and framework:
Anthropic's native desktop app for Claude with built-in MCP support.
AI-first code editor with integrated LLM-powered coding assistance.
GitHub Copilot in VS Code with Agent mode and MCP support.
Purpose-built IDE for agentic AI coding workflows.
Autonomous AI coding agent that runs inside VS Code.
Anthropic's agentic CLI for terminal-first development.
Python SDK for building production-grade OpenAI agent workflows.
Google's framework for building production AI agents.
Type-safe agent development for Python with first-class MCP support.
TypeScript toolkit for building AI-powered web applications.
TypeScript-native agent framework for modern web stacks.
Python framework for orchestrating collaborative AI agent crews.
Leading Python framework for composable LLM applications.
Data-aware AI agent framework for structured and unstructured sources.
Microsoft's framework for multi-agent collaborative conversations.
Connect Elastic Enterprise Search to LlamaIndex
Get your token, paste the configuration, and start using 6 tools in under 2 minutes. No API key management needed.