How to Use the CHATFLY MCP in AutoGen

Debate support strategies by connecting CHATFLY to your AutoGen multi-agent system.

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Connect CHATFLY MCP to AutoGen

Create your Vinkius account to connect CHATFLY 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 CHATFLY with AutoGen

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Negotiate CHATFLY actions in AutoGen

Let your security agent verify `trigger_bot_training` requests before a performance agent executes them. You force your agents to debate the impact of training on your system resources. This MCP server provides the tools that your agents argue over. You get a consensus-driven approach to managing your bot fleet.

Monitor CHATFLY conversations in AutoGen

Use `list_fly_conversations` as a starting point for your agents to analyze support quality. One agent summarizes the chat, while another critiques the bot's helpfulness. You build a review cycle where agents challenge each other's assessment of support quality. It turns raw logs into a debate about your customer experience.

Manage CHATFLY bots with agent teams

Task a management agent to call `list_chatfly_bots` and `get_chatbot_details` to audit your account. It flags inactive bots or misconfigured settings for your team to review. Your agents work together to ensure your bot fleet remains healthy. They handle the routine checks, so you only intervene when there is a real problem.

Secure CHATFLY operations in AutoGen

Set up a specialized agent to call `get_chatfly_account_info` to monitor quota usage across your organization. If limits are reached, the agent triggers a notification to your team. This keeps your operations running within budget without manual oversight. Your agents act as the first line of defense for your support infrastructure.

Setup guide

Set up CHATFLY 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 CHATFLY 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="CHATFLY_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

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

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

Absolutely, you pass the tools to your agents, and they can negotiate which ones to use. They evaluate the results of each call as part of their decision process.

You use the MCP tool adapter to convert the server tools into AutoGen functions. The agents then call them as part of their standard conversation loop.

Yes, agents can list, monitor, and trigger training for your bots. You define the policy, and the agents execute it.

You can. By giving multiple agents access to the same tools, they collaborate to resolve complex customer issues.

Security is handled at the transport layer. Since the server uses an ephemeral token, your sensitive support data is never stored in the agents themselves, only accessed during the conversation.

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