How to Use the Zoho Campaign MCP in AutoGen

Q: How can I get a full performance picture using Zoho Campaign with AutoGen?

You initialize a debate between two agents. One agent runs getcampaignreport to pull the raw metrics (opened, clicked). The second agent then compares those results against the overall portfolio view from getcampaigns, identifying trends and outliers.

Q: What information is available when listing all mailing lists using Zoho Campaign with AutoGen?

The agents review getlists results. They can debate which list should be used for the next campaign, based on criteria like 'highest contact count' or 'newest creation date.'

Resolve complex marketing tasks through AutoGen's multi-agent collaboration with Zoho Campaign.

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Connect Zoho Campaign MCP to AutoGen

Create your Vinkius account to connect Zoho Campaign 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 Campaign with AutoGen

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Perplexity

Campaign Strategy Debate via the MCP Server

Need to launch a tricky campaign? You set up multiple agents—one 'Compliance Agent,' one 'Performance Agent.' They discuss using `get_campaign_details` and challenge each other's proposed subject lines or sender names before you commit to running `create_campaign`. This consensus-driven process means the final decision is vetted, reducing human error in complex marketing deployments.

Complex Contact Management

Handling unsubscribes? You don't just run a removal command. The agents debate: 'Should we remove all contacts from list X?' versus 'We should only remove those marked as bounced.' They use `get_blocked_contacts` to validate the scope, then decide on the final list of emails for `remove_contacts_from_list`. This deliberation ensures you manage unsubscribes accurately and don't accidentally wipe out active subscribers.

Reporting and Validation

Before a send, an agent pair can validate. One agent pulls the full report using `get_campaign_report` (checking for high bounce rates). The second agent then calls `get_contact` on specific users to verify if those individuals were included in the campaign's recipient list. This negotiation process confirms data integrity across different API endpoints.

Setup guide

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

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

    result = await agent.run("List recent Zoho Campaign 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 Campaign. 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 Campaign MCP in AutoGen

You initialize a debate between two agents. One agent runs `get_campaign_report` to pull the raw metrics (opened, clicked). The second agent then compares those results against the overall portfolio view from `get_campaigns`, identifying trends and outliers.

The agents work together. One agent pulls a list of active subscribers via `get_list_contacts`. The other confirms that those users are not present in the records returned by `get_blocked_contacts`, ensuring clean targeting.

The agents debate the best time to send. They might first call `get_campaigns` to check for conflicts or existing drafts, and then use the details gathered to finalize parameters before calling `create_campaign`.

The agents review `get_lists` results. They can debate which list should be used for the next campaign, based on criteria like 'highest contact count' or 'newest creation date.'

The server touches contact information (email, name, phone). When setting up the multi-agent system, you can define roles and constraints. For example, giving one agent read-only access to `get_contact` prevents accidental modification of sensitive data.

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