How to Use the ChatBot.com MCP in AutoGen

Q: How do AutoGen agents collaborate to analyze ChatBot.com story interactions?

One agent uses liststoryinteractions to collect raw user paths, while a critic agent evaluates the drop-off rates. They discuss the findings in a group chat to pinpoint exactly where users lose interest.

Q: Can an AutoGen agent group manage ChatBot.com webhooks autonomously?

Yes, you can register listchatbotwebhooks as a tool for your assistant agents. They can monitor endpoint statuses and debate security configurations if an unexpected error occurs.

Q: How do I register ChatBot.com MCP tools with an AutoGen agent?

Use the mcpservertools adapter from the autogen-ext library to import the tools. Once imported, pass the tool array directly to your AssistantAgent constructor.

Q: How is my ChatBot.com webhook data and story interactions protected in AutoGen?

Your webhook URLs from listchatbotwebhooks and interaction histories from liststoryinteractions are processed in memory within Vinkius's secure sandboxed environment. No raw data is cached or exposed to external LLM providers during the agent debate.

Orchestrate AutoGen agents that debate, refine, and optimize ChatBot.com story paths and user flows.

See Vinkius in Action

Works with every AI agent you already use

…and any MCP-compatible client

MCP Servers - Free for Subscribers

Connect ChatBot.com MCP to AutoGen

Create your Vinkius account to connect ChatBot.com 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.

GDPR Free for Subscribers

Setup ChatBot.com with AutoGen

Ask AI about this MCP

ChatGPT

Claude

Perplexity

Let AutoGen agents audit ChatBot.com conversation paths

The `list_chatbot_stories` tool allows your primary auditor agent to inspect all active workflows. A second critic agent then analyzes these paths to find dead ends or loops that frustrate customers. By feeding `get_story_details` to this multi-agent group, they can debate whether a flow is too long. They reach a consensus on changes before you deploy the updates to production.

Resolve ChatBot.com webhook errors via agent debate

The `list_chatbot_webhooks` tool fetches active integration endpoints for your system agents to inspect. If a webhook fails, your developer agent and security agent coordinate to diagnose the payload structure. Using this MCP Server, the agents cross-reference the webhook configuration with live interaction logs. They resolve endpoint mismatches dynamically, ensuring your backend integrations never drop a message.

Train ChatBot.com intents using AutoGen agent consensus

The `list_training_data` tool exposes unmatched customer phrases to your specialized training agents using this MCP Server integration. One agent drafts a new NLP intent while another agent challenges its accuracy against existing entities. This collaborative process ensures that only high-quality, non-overlapping phrases are recommended. They cross-reference `list_chatbot_entities` to guarantee the new intent won't conflict with your current bot setup.

Setup guide

Set up ChatBot.com 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 ChatBot.com 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="ChatBot.com_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

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

    result = await agent.run("List recent ChatBot.com 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 ChatBot. 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.

Why Choose Vinkius

Vinkius connects your tools to AI with real-time monitoring and automatic cost savings — all from one dashboard.

Connect ChatBot.com now

Real-time monitoring

Live

visibility into every interaction

Connect your favorite tools to your AI and see exactly what's happening — every request, every response, in real time.

Built-in savings

60%

lower AI costs

Vinkius compresses data between your apps and your AI automatically. Lower bills every month — no configuration required.

Single dashboard

One

place for every integration

Every tool your AI connects to, managed from a single screen. One account, complete control.

Common questions about ChatBot.com MCP in AutoGen

One agent uses `list_story_interactions` to collect raw user paths, while a critic agent evaluates the drop-off rates. They discuss the findings in a group chat to pinpoint exactly where users lose interest.

Yes, you can register `list_chatbot_webhooks` as a tool for your assistant agents. They can monitor endpoint statuses and debate security configurations if an unexpected error occurs.

Use the `mcp_server_tools` adapter from the `autogen-ext` library to import the tools. Once imported, pass the tool array directly to your AssistantAgent constructor.

No, Vinkius handles the underlying authentication for the MCP Server. Your AutoGen agents connect to a single endpoint, using one token to access all eight tools.

Your webhook URLs from `list_chatbot_webhooks` and interaction histories from `list_story_interactions` are processed in memory within Vinkius's secure sandboxed environment. No raw data is cached or exposed to external LLM providers during the agent debate.

Use it with your favorite AI tools

Connect this server to Cursor, Claude, VS Code, and more.

OpenAI Agents SDK sdk-python

Google ADK sdk-python

Pydantic AI sdk-python

Vercel AI SDK sdk-typescript