How to Use the Cloze MCP in AutoGen

Q: How do AutoGen agents handle custom fields in Cloze?

An agent calls listcustomfields to inspect your CRM schema. It then presents these fields to the coordinator agent, ensuring any new deal created via createproject matches your system.

Deploy AutoGen multi-agent squads that debate lead quality and collaborate to update your Cloze CRM via this MCP server.

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Connect Cloze MCP to AutoGen

Create your Vinkius account to connect Cloze 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 Cloze with AutoGen

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Multi-Agent Deal Negotiation in AutoGen

The `create_project` tool allows your AutoGen sales agent to log new deals in Cloze using this MCP tool after a consensus is reached with your finance agent. The agents debate the deal terms in a chat thread before committing the data. Once the agents agree that the budget matches your margin requirements, they trigger the tool to record the property or deal details automatically.

Collaborative Contact Validation

The `get_person` tool fetches Cloze profiles so your AutoGen verification agent can audit contact data. If the email or name is incomplete, the verification agent asks the writing agent to fix it. The writing agent then calls `update_person` to save the verified details back to Cloze, ensuring no low-quality or corrupt contact records enter your CRM.

Automated Tag Assignment Debates

The `search_people_by_tag` tool lets an AutoGen marketing agent analyze segmented lists of contacts in Cloze. The agent discusses target profiles with a product agent to decide if a tag needs changing. They use the consensus from their conversation to clean up your CRM tags, keeping your marketing lists accurate without human intervention.

Setup guide

Set up Cloze 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 Cloze 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="Cloze_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

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

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

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

You register the Cloze MCP tools with your AssistantAgent. Both your sales and marketing agents can call `list_people` or `update_person` during their group chats to share CRM context.

Yes. If two agents try to modify the same contact, they debate the correct details using `get_person` before one agent finally calls `update_person` to commit the agreed changes.

An agent calls `list_custom_fields` to inspect your CRM schema. It then presents these fields to the coordinator agent, ensuring any new deal created via `create_project` matches your system.

Yes. Your agents can monitor a shared inbox, debate whether a lead is qualified, and call `create_project` to log the deal in Cloze once they reach a consensus.

Your CRM contact details, email addresses, and deal records are only shared within your local AutoGen session. Vinkius executes the MCP server tools in an ephemeral, zero-trust sandbox that never persists your CRM data.

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