How to Use the Cloudinary MCP in AutoGen

Q: How do AutoGen agents select Cloudinary upload presets?

One agent calls listuploadpresets to get the options, and then argues with your billing agent to select the most cost-effective preset.

Q: Can an AutoGen conversation monitor Cloudinary usage?

Yes, a monitoring agent can run getcloudinaryusagereport and alert the rest of the agent group if bandwidth or storage limits are exceeded.

Let your AutoGen agents debate asset usage, manage transformations, and coordinate media cleanups through Cloudinary.

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Connect Cloudinary MCP to AutoGen

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

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Setup Cloudinary with AutoGen

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Coordinate media cleanup via agent debate

Your AutoGen agents use `list_media_resources` to retrieve your asset catalog and debate which files are safe to delete. A storage auditor agent flags high-cost videos, while a product agent defends assets currently used in active marketing campaigns. Once they reach a consensus, the coordinator agent calls `delete_media_resource` to remove the approved files. This multi-agent debate ensures you never delete critical media assets by mistake.

Manage Cloudinary transformations via AutoGen and MCP

Your design agent and performance agent use `list_media_transformations` to negotiate the best layout for your site's images. The performance agent argues for aggressive compression, while the design agent insists on high-resolution presets. They review the available named transformations together and agree on a configuration that satisfies both speed and aesthetic requirements. This collaborative decision-making process ensures your media assets are optimized without sacrificing visual quality.

Monitor usage limits with cooperative agents

A billing agent uses this MCP Server to call `get_cloudinary_usage_report` to check your current bandwidth consumption and shares the report with your upload agent. If you are close to your monthly limit, the billing agent negotiates a temporary freeze on heavy video uploads. The upload agent checks `list_upload_presets` to find low-bandwidth alternatives that fit within your remaining quota. Your agents work together to keep your application running smoothly without incurring unexpected overage charges.

Setup guide

Set up Cloudinary MCP in AutoGen

Prerequisites

Python 3.10+ installed
autogen-ext[mcp] package
Active Vinkius subscription with a valid endpoint token

1

Install AutoGen with MCP
Run pip install "autogen-ext[mcp]" autogen-agentchat. The MCP extension includes mcp_server_tools for stateless tool access.
2

Fetch tools from the MCP
Call mcp_server_tools(SseServerParams(url=...)) with your Vinkius endpoint. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.
3

Run your agent
Pass the tools to AssistantAgent and call agent.run(). The agent invokes Cloudinary tools and returns structured results.

agent.py

from autogen_ext.tools.mcp import SseServerParams, mcp_server_tools
from autogen_agentchat.agents import AssistantAgent
from autogen_ext.models.openai import OpenAIChatCompletionClient

server_params = SseServerParams(
    url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)

tools = await mcp_server_tools(server_params)

agent = AssistantAgent(
    name="Cloudinary_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

result = await agent.run("List recent Cloudinary data")
print(result.messages[-1].content)

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Prerequisites

Python 3.10+ installed
autogen-ext[mcp] + autogen-agentchat
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Same packages as above. McpWorkbench is ideal when your agent needs stateful sessions across multiple tool calls.
2

Use McpWorkbench as context manager
Wrap your agent in async with McpWorkbench(...) to maintain shared state and resources. The workbench manages the full MCP session lifecycle.
3

Run with workbench
Pass workbench=workbench to your agent. State is preserved across multiple tool calls within the same session.

agent.py

from autogen_ext.tools.mcp import McpWorkbench, SseServerParams
from autogen_agentchat.agents import AssistantAgent
from autogen_ext.models.openai import OpenAIChatCompletionClient

server_params = SseServerParams(
    url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)

async with McpWorkbench(server_params) as workbench:
    agent = AssistantAgent(
        name="Cloudinary_assistant",
        model_client=OpenAIChatCompletionClient(model="gpt-4o"),
        workbench=workbench,
    )

    result = await agent.run("List recent Cloudinary data")
    print(result.messages[-1].content)

Get your connection token →

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Common questions about Cloudinary MCP in AutoGen

Yes, your agents can debate which files to purge based on metadata retrieved from `get_media_resource_details` before calling the deletion tool.

One agent calls `list_upload_presets` to get the options, and then argues with your billing agent to select the most cost-effective preset.

Yes, a monitoring agent can run `get_cloudinary_usage_report` and alert the rest of the agent group if bandwidth or storage limits are exceeded.

You use the AutoGen MCP extension to register the server's tools and pass them directly to your AssistantAgent constructor.

This MCP Server only handles your Cloudinary media metadata, usage metrics, and transformation configurations. It runs inside a secure, sandboxed Vinkius environment, so your private media credentials and API tokens never leave your local session.

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