Elemeno MCP Server for LlamaIndex 10 tools — connect in under 2 minutes
LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add Elemeno as an MCP tool provider through 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 Elemeno. "
"You have 10 tools available."
),
)
response = await agent.run(
"What tools are available in Elemeno?"
)
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 Elemeno MCP Server
Integrate Elemeno, the headless CMS designed for developers, directly into your AI workflow. Manage your content collections and singletons (global pages), track individual items and their publishing statuses, monitor field schemas, and oversee your entire content library using natural language.
LlamaIndex agents combine Elemeno tool responses with indexed documents for comprehensive, grounded answers. Connect 10 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.
What you can do
- Collection Oversight — List and retrieve detailed information and field schemas for all your content collections.
- Content Intelligence — Monitor individual collection items, resolving titles, slugs, and real-time publishing statuses.
- Singleton Management — Access and monitor singleton content blocks, resolving global settings and unique page data.
- Content Auditing — Retrieve high-level summaries of collection activity, item counts, and organizational CMS health.
The Elemeno MCP Server exposes 10 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 Elemeno to LlamaIndex via MCP
Follow these steps to integrate the Elemeno 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 10 tools from Elemeno
Why Use LlamaIndex with the Elemeno MCP Server
LlamaIndex provides unique advantages when paired with Elemeno through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine Elemeno tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain Elemeno tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query Elemeno, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what Elemeno tools were called, what data was returned, and how it influenced the final answer
Elemeno + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the Elemeno MCP Server delivers measurable value.
Hybrid search: combine Elemeno real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query Elemeno 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 Elemeno for fresh data
Analytical workflows: chain Elemeno queries with LlamaIndex's data connectors to build multi-source analytical reports
Elemeno MCP Tools for LlamaIndex (10)
These 10 tools become available when you connect Elemeno to LlamaIndex via MCP:
get_collection_details
Get detailed settings and field schema for a specific collection
get_collection_item_details
Get full content and metadata for a specific item in a collection
get_elemeno_account_metadata
Retrieve metadata and limits for your Elemeno account
get_singleton_content
Get the full content data for a specific singleton
list_collection_items
List all content items within a specific collection
list_content_collections
List all content collections configured in your Elemeno account
list_content_singletons
List all singleton content blocks (unique global pages/settings)
list_published_content
Identify items that are currently in a "Published" status
quick_content_volume_audit
Retrieve a high-level summary of collection and singleton activity
search_collection_content
Search for items within a collection using a title or slug keyword
Example Prompts for Elemeno in LlamaIndex
Ready-to-use prompts you can give your LlamaIndex agent to start working with Elemeno immediately.
"List all content collections in my project."
"Show me the items in the 'Services' collection."
"What is the content of the 'Contact Page' singleton?"
Troubleshooting Elemeno MCP Server with LlamaIndex
Common issues when connecting Elemeno to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpElemeno + LlamaIndex FAQ
Common questions about integrating Elemeno 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 Elemeno 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 Elemeno to LlamaIndex
Get your token, paste the configuration, and start using 10 tools in under 2 minutes. No API key management needed.