Kontent.ai 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 Kontent.ai 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 Kontent.ai. "
"You have 10 tools available."
),
)
response = await agent.run(
"What tools are available in Kontent.ai?"
)
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 Kontent.ai MCP Server
Connect your AI agent to Kontent.ai Delivery API to fetch and analyze your modular content.
LlamaIndex agents combine Kontent.ai 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.
Key Features
- Content Item Retrieval — Fetch the full modular content of any item by its codename
- Schema Auditing — List and examine content types to understand your project's data model
- Taxonomy Access — Query taxonomy groups and terms for content categorization
- Asset Discovery — Locate images and files stored in your content library
- Smart Search — Perform filtered searches across your entire delivery repository
Simple Setup
1. Subscribe to this server
2. Get your Project ID from Kontent.ai (Project Settings > API keys)
3. (Optional) Enter your Delivery API Key if Secure Access is enabled
4. Start querying your content via natural language
The Kontent.ai 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 Kontent.ai to LlamaIndex via MCP
Follow these steps to integrate the Kontent.ai 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 Kontent.ai
Why Use LlamaIndex with the Kontent.ai MCP Server
LlamaIndex provides unique advantages when paired with Kontent.ai through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine Kontent.ai tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain Kontent.ai tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query Kontent.ai, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what Kontent.ai tools were called, what data was returned, and how it influenced the final answer
Kontent.ai + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the Kontent.ai MCP Server delivers measurable value.
Hybrid search: combine Kontent.ai real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query Kontent.ai 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 Kontent.ai for fresh data
Analytical workflows: chain Kontent.ai queries with LlamaIndex's data connectors to build multi-source analytical reports
Kontent.ai MCP Tools for LlamaIndex (10)
These 10 tools become available when you connect Kontent.ai to LlamaIndex via MCP:
get_content_item
Get a specific content item by codename
get_content_type
Get details for a content type
get_content_type_element
g., options for a multiple choice element). Get metadata for a specific element in a type
get_taxonomy_group
Get details for a taxonomy group
list_content_assets
ai. Query assets from the content library
list_content_items
Use this to find codenames for specific articles, products, or pages. List all content items from Kontent.ai
list_content_types
List all content types (schemas)
list_project_languages
List supported languages
list_taxonomies
List taxonomy groups
search_kontent_ai
Search for content using query parameters
Example Prompts for Kontent.ai in LlamaIndex
Ready-to-use prompts you can give your LlamaIndex agent to start working with Kontent.ai immediately.
"List the last 10 content items in Kontent.ai"
"Show the schema for content type 'article'"
"Search for items related to 'Winter Sale'"
Troubleshooting Kontent.ai MCP Server with LlamaIndex
Common issues when connecting Kontent.ai to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpKontent.ai + LlamaIndex FAQ
Common questions about integrating Kontent.ai 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 Kontent.ai 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 Kontent.ai to LlamaIndex
Get your token, paste the configuration, and start using 10 tools in under 2 minutes. No API key management needed.