How to Use the Zoho WorkDrive MCP in AutoGen

Q: If I want to list files in Zoho WorkDrive using AutoGen, how does it work?

You assign one agent the task of listing. It executes listfiles, and then other agents can validate or refine that list, adding layers of oversight.

Build consensus systems with AutoGen. Let competing agents debate Zoho WorkDrive file decisions.

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Connect Zoho WorkDrive MCP to AutoGen

Create your Vinkius account to connect Zoho WorkDrive 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 Zoho WorkDrive with AutoGen

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AutoGen: Debate File Permissions

You can set up a security agent and an efficiency agent to argue over access rights. The security agent calls `list_file_permissions` to flag potential risks, while the efficiency agent proposes optimizing sharing links using `create_share_link`. The result isn't just one action; it's a consensus on the safest way forward, based on deliberation between two competing perspectives.

AutoGen: Structured Workspace Decisions

Need to reorganize files? Have your agents debate the best approach. One agent might suggest deleting an old folder using `delete_file`, while another argues that they should first archive it or rename it (`rename_file`). The multi-agent framework forces a discussion, ensuring you don't lose data just because one process ran too fast.

AutoGen: Multi-Step File Management

An agent could be assigned the task of onboarding a new team. The workflow involves several steps: first checking all existing teams (`list_teams`), then creating a dedicated workspace folder (`create_folder`), and finally assigning initial permissions (`list_file_permissions`). The agents discuss each step, verifying the outcome before moving to the next tool call.

Setup guide

Set up Zoho WorkDrive 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 Zoho WorkDrive 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="Zoho WorkDrive_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

result = await agent.run("List recent Zoho WorkDrive 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="Zoho WorkDrive_assistant",
        model_client=OpenAIChatCompletionClient(model="gpt-4o"),
        workbench=workbench,
    )

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

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 Zoho WorkDrive. 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.

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Common questions about Zoho WorkDrive MCP in AutoGen

You give your agents the goal (e.g., 'Find the latest version of X'). They debate which tools to use—`get_file_metadata` is usually key here—and converge on the necessary data points.

You assign one agent the task of listing. It executes `list_files`, and then other agents can validate or refine that list, adding layers of oversight.

Yes. You can simulate a planning session where multiple agents argue over the best name and location for a new directory using `create_folder` before committing to the action.

The server handles various structured object types: file metadata, team lists, permission settings, and share link details. The agents negotiate over these specific data points.

Absolutely. You can set up a debate where one agent suggests a new naming convention (e.g., adding project codes) and another confirms the target file exists before calling `rename_file`.

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