How to Use the Cliengo MCP in AutoGen

Q: How do I pass Cliengo MCP Server tools to an AutoGen agent?

Use mcpservertools with StreamableHttpServerParams pointing to your Vinkius URL. Pass the resulting tool list directly into your AssistantAgent constructor.

Q: Does the adapter convert the tool schemas automatically?

McpToolAdapter handles the entire translation layer. The agents see native Python functions for operations like fetching user lists without any manual wrapping.

Give your AutoGen multi-agent teams direct access to Cliengo lead data and chat transcripts.

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Connect Cliengo MCP to AutoGen

Create your Vinkius account to connect Cliengo 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 Cliengo with AutoGen

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Perplexity

AutoGen teams for lead scoring

Single agents miss nuance. You build a system where a researcher agent uses `list_cliengo_leads` to find new prospects, while a critic agent reviews the findings against your ideal customer profile. The researcher calls `get_lead_details` to present facts. The critic debates the quality of the lead based on company criteria, forcing the group to reach a consensus before pushing the data to your CRM.

Analyze transcripts through debate

Customer support audits require multiple perspectives. One agent pulls a frustrated customer's thread using `get_chat_history` and argues the bot failed to answer the question. A secondary agent, acting as a policy expert, reads the exact same output from `get_contact_messages` and defends the routing logic. They negotiate a final summary of what actually went wrong.

Audit infrastructure collaboratively

Migrating chat widgets takes careful planning. You assign an infrastructure agent to map out current deployments via `list_cliengo_websites`. A security-focused agent then reviews the routing setups by calling `list_cliengo_webhooks`. If the two agents disagree on whether a specific project is compliant, they deliberate until they resolve the conflict.

Setup guide

Set up Cliengo 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 Cliengo 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="Cliengo_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

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

Get your connection token →

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="Cliengo_assistant",
        model_client=OpenAIChatCompletionClient(model="gpt-4o"),
        workbench=workbench,
    )

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

Use `mcp_server_tools` with `StreamableHttpServerParams` pointing to your Vinkius URL. Pass the resulting tool list directly into your `AssistantAgent` constructor.

Yes. You provide the same tool list to any agent in the chat that needs it. One runs `list_chat_conversations` while another handles the analysis.

`McpToolAdapter` handles the entire translation layer. The agents see native Python functions for operations like fetching user lists without any manual wrapping.

You typically design a summarizer agent to handle the heavy lifting. It fetches the transcript, condenses it, and feeds only the bullet points to the rest of the group.

Data transit happens strictly over an authenticated, ephemeral Vinkius tunnel. Your agents access real names and contact histories during their debate, but the MCP endpoint itself retains zero state after the session ends.

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