Cloudinary MCP Server for LlamaIndex 8 tools — connect in under 2 minutes
LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add Cloudinary as an MCP tool provider through Vinkius and your agents can query, analyze, and act on live data alongside your existing indexes.
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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 Cloudinary. "
"You have 8 tools available."
),
)
response = await agent.run(
"What tools are available in Cloudinary?"
)
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 Cloudinary MCP Server
Connect your Cloudinary account to any AI agent and take full control of your media library through natural conversation. Streamline how you manage, optimize, and distribute images and videos natively.
LlamaIndex agents combine Cloudinary tool responses with indexed documents for comprehensive, grounded answers. Connect 8 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
- Resource Oversight — List and retrieve details for all media resources including public IDs, formats, and secure URLs natively
- Usage Intelligence — Access core usage and quota reports for storage, bandwidth, and transformations flawlessly
- Asset Logistics — Monitor tags, folders, and transformations used across your media library securely
- Search Management — Perform advanced searches using complex expressions to find specific assets instantly flawlessly
- Automation Logistics — List configured upload presets to ensure consistent asset ingestion flawlessly
- Content Control — Permanently delete unwanted media resources directly from your chat interface flawlessly
The Cloudinary MCP Server exposes 8 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 Cloudinary to LlamaIndex via MCP
Follow these steps to integrate the Cloudinary 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 8 tools from Cloudinary
Why Use LlamaIndex with the Cloudinary MCP Server
LlamaIndex provides unique advantages when paired with Cloudinary through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine Cloudinary tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain Cloudinary tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query Cloudinary, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what Cloudinary tools were called, what data was returned, and how it influenced the final answer
Cloudinary + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the Cloudinary MCP Server delivers measurable value.
Hybrid search: combine Cloudinary real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query Cloudinary 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 Cloudinary for fresh data
Analytical workflows: chain Cloudinary queries with LlamaIndex's data connectors to build multi-source analytical reports
Cloudinary MCP Tools for LlamaIndex (8)
These 8 tools become available when you connect Cloudinary to LlamaIndex via MCP:
delete_media_resource
Permanently delete a media resource from the cloud
get_cloudinary_usage_report
Retrieve core usage and quota information (Storage, Bandwidth, Transformations)
get_media_resource_details
Get detailed information for a specific media resource
list_media_resources
List all media resources (images, videos) in the cloud
list_media_tags
List all tags used in the media library
list_media_transformations
List all named and dynamic transformations
list_upload_presets
List all configured upload presets
search_media_library
Search for resources using a search expression
Example Prompts for Cloudinary in LlamaIndex
Ready-to-use prompts you can give your LlamaIndex agent to start working with Cloudinary immediately.
"List all images in my Cloudinary library."
"What is my current Cloudinary storage usage?"
"Search for all MP4 videos uploaded in the last 24 hours."
Troubleshooting Cloudinary MCP Server with LlamaIndex
Common issues when connecting Cloudinary to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpCloudinary + LlamaIndex FAQ
Common questions about integrating Cloudinary 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 Cloudinary 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.
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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 Cloudinary to LlamaIndex
Get your token, paste the configuration, and start using 8 tools in under 2 minutes. No API key management needed.