How to Use the Cloudinary MCP in LlamaIndex

Q: How do I index Cloudinary assets into LlamaIndex?

You call listmediaresources to fetch your asset list, then load the resulting metadata into a LlamaIndex document store. From there, you build a vector index for semantic queries.

Q: Can LlamaIndex search Cloudinary transformations semantically?

Yes, by indexing the output of listmediatransformations, your LlamaIndex agent can search your transformation list using natural language to find the closest match.

Q: Does LlamaIndex support Cloudinary upload presets?

Your agent can query listuploadpresets to verify available upload configurations. It then indexes these presets so your RAG pipeline knows which upload rules to apply.

Index your Cloudinary asset catalog directly into LlamaIndex to query your media library using semantic search.

See Vinkius in Action

Works with every AI agent you already use

…and any MCP-compatible client

MCP Servers - Free for Subscribers

Connect Cloudinary MCP to LlamaIndex

Create your Vinkius account to connect Cloudinary to LlamaIndex and route execution through our secure gateway. The platform manages server hosting, runtime updates, and security layers. Configuration requires no manual server provisioning.

GDPR Free for Subscribers

Setup Cloudinary with LlamaIndex

Ask AI about this MCP

ChatGPT

Claude

Perplexity

Index Cloudinary asset metadata for RAG

This MCP Server allows your LlamaIndex pipeline to pull media metadata using `get_media_resource_details` and store it in a vector database. Your agent then queries this index to find specific assets without hitting the live API repeatedly. By converting raw asset details into searchable text nodes, your agent can answer complex questions about your media library. This setup grounds your agent's answers in actual asset data, eliminating hallucinations about your image catalog.

Search Cloudinary tags using LlamaIndex

Your LlamaIndex agent calls `list_media_tags` to retrieve all active tags across your media library and builds a semantic index. Users can search for general concepts, and the agent maps those queries to your actual Cloudinary tags. The indexed tags allow your query engine to retrieve matching assets using vector similarity. You get a search system that understands user intent instead of relying on exact string matches.

Track transformation configurations in your index

Your agent uses `list_media_transformations` via this MCP Server to load all named and dynamic transformations into your LlamaIndex document store. The agent inspects this index to determine which image transformations are already available before requesting a new one. Storing these configurations in a local index accelerates decision-making for your image processing pipelines. Your agent can instantly check if a specific crop or filter exists without making redundant network calls.

Setup guide

Set up Cloudinary MCP in LlamaIndex

Prerequisites

Python 3.10+ installed
llama-index-tools-mcp package
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install llama-index-tools-mcp llama-index-llms-openai. The MCP tools package provides BasicMCPClient and McpToolSpec.
2

Connect with BasicMCPClient
Point BasicMCPClient to your Vinkius endpoint URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. Supports SSE and Streamable HTTP transports.
3

Convert to LlamaIndex tools
Call mcp_tool_spec.to_tool_list_async() to convert all Cloudinary MCP tools into native FunctionTool objects that any LlamaIndex agent can use.
4

Run with any LLM
Create a FunctionAgent with the tools and your preferred LLM. Swap OpenAI for Anthropic, Gemini, or any LlamaIndex-supported provider.

agent.py

from llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI

# Connect to the MCP
mcp_client = BasicMCPClient(
    "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)
mcp_tool_spec = McpToolSpec(client=mcp_client)

# Convert MCP tools to LlamaIndex tools
tools = await mcp_tool_spec.to_tool_list_async()

# Create and run the agent
agent = FunctionAgent(
    tools=tools,
    llm=OpenAI(model="gpt-4o"),
    system_prompt="You have access to Cloudinary tools.",
)
response = await agent.run("List recent Cloudinary data")

Get your connection token →

Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Cloudinary. 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.

Why Choose Vinkius

Vinkius connects your tools to AI with real-time monitoring and automatic cost savings — all from one dashboard.

Connect Cloudinary now

Real-time monitoring

Live

visibility into every interaction

Connect your favorite tools to your AI and see exactly what's happening — every request, every response, in real time.

Built-in savings

60%

lower AI costs

Vinkius compresses data between your apps and your AI automatically. Lower bills every month — no configuration required.

Single dashboard

One

place for every integration

Every tool your AI connects to, managed from a single screen. One account, complete control.

Common questions about Cloudinary MCP in LlamaIndex

You call `list_media_resources` to fetch your asset list, then load the resulting metadata into a LlamaIndex document store. From there, you build a vector index for semantic queries.

Yes, by indexing the output of `list_media_transformations`, your LlamaIndex agent can search your transformation list using natural language to find the closest match.

Your agent can query `list_upload_presets` to verify available upload configurations. It then indexes these presets so your RAG pipeline knows which upload rules to apply.

The server paginates through your resources, allowing LlamaIndex to ingest your catalog in batches. This keeps memory usage low while building your vector index.

The MCP Server only accesses your Cloudinary asset metadata, transformation rules, and usage metrics. All data is processed in an isolated Vinkius container, meaning your private API keys and image URLs are never exposed or stored.

Use it with your favorite AI tools

Connect this server to Cursor, Claude, VS Code, and more.

OpenAI Agents SDK sdk-python

Google ADK sdk-python

Pydantic AI sdk-python

Vercel AI SDK sdk-typescript