How to Use the HelpCrunch MCP in AutoGen

Q: How do my AutoGen agents access the HelpCrunch MCP tools?

You use the mcpservertools helper from the AutoGen MCP extension. This automatically maps tools like sendmessage to schemas that your agents can understand and invoke during conversations.

Deploy AutoGen multi-agent teams that debate and coordinate to resolve HelpCrunch tickets and assign tasks.

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Create your Vinkius account to connect HelpCrunch 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 HelpCrunch with AutoGen

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Coordinate support teams with AutoGen and MCP Server

Multi-agent AutoGen teams excel when different agents handle specialized HelpCrunch support roles. One AutoGen agent can monitor the queue using `list_chats` while another agent focuses entirely on analyzing HelpCrunch customer history. They coordinate their findings before taking action on the HelpCrunch platform. Once they agree on the issue, the coordinator AutoGen agent triggers `update_chat_assignee` to hand the HelpCrunch ticket to the right human specialist.

Debate ticket categorization before tagging

Mistagging a HelpCrunch customer profile can ruin your marketing metrics, which is why AutoGen uses collaborative validation. In an AutoGen setup, a triage agent proposes a tag after reading HelpCrunch transcripts with `list_chat_messages`, but a quality agent must approve it. Only after both AutoGen agents reach consensus does the system execute `add_customer_tag` in HelpCrunch. This debate loop prevents automated HelpCrunch mistakes and keeps your database clean.

Resolve complex chats through agent collaboration

Some customer issues require deep investigation across HelpCrunch departments, making them perfect for AutoGen collaboration. Your AutoGen agents can search past records using `search_chats` and check HelpCrunch department availability via `list_departments`. They draft a HelpCrunch response together, refining the technical details through AutoGen chat loops. Once the draft is approved, the dispatch AutoGen agent uses `send_message` to send the fix and runs `update_chat_status` to close the HelpCrunch loop.

Setup guide

Set up HelpCrunch 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 HelpCrunch 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="HelpCrunch_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

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

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

You use the `mcp_server_tools` helper from the AutoGen MCP extension. This automatically maps tools like `send_message` to schemas that your agents can understand and invoke during conversations.

Yes, they can debate the resolution of a ticket. Once the supervisor agent confirms the issue is fixed, it commands the worker agent to run `update_chat_status`.

The framework handles tool calls sequentially or in parallel depending on your agent workflow configuration. Tools like `list_customers` can be run alongside chat searches to save execution time.

You should set up a dedicated fallback agent. If a tool call like `update_chat_assignee` fails due to an invalid ID, the fallback agent catches the error and alerts your team.

All customer names, email addresses, and message histories retrieved via `get_customer_details` are confined to your runtime environment. Vinkius runs the MCP connector in an isolated sandbox, keeping your credentials encrypted.

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