How to Use the Front MCP in AutoGen

Q: How do I register Front tools with AutoGen agents?

You pass the tools list from mcpservertools directly into your AutoGen AssistantAgent constructor. The framework handles the JSON schema conversions automatically so your agents can immediately call listsharedinboxes via MCP.

Coordinate multi-agent debates in AutoGen to review, approve, and send Front email replies.

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Setup Front with AutoGen

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Run consensus-driven Front email triage in AutoGen

Your AutoGen triage agent runs `get_conversation_details` to analyze a Front ticket before letting other agents debate the issue. A quality agent drafts a response and checks it against team guidelines using our MCP tools. Only after both AutoGen agents agree on the draft does the executor agent call `send_inbox_reply` to message the customer. This collaborative workflow prevents single-agent hallucinations from reaching your users' Front inboxes.

Manage Front shared inboxes via collaborative AutoGen agents

You split responsibilities across AutoGen agents by assigning `list_inbox_threads` to a monitoring agent and `update_conversation_status` to a routing agent. This keeps your agents focused on single tasks instead of running massive, expensive context windows. These AutoGen agents communicate in a shared group chat to coordinate their efforts. For example, the routing agent will alert the team when a Front ticket is unassigned, prompting the assignment agent to fetch teammates using `list_inbox_teammates`.

Resolve complex Front customer issues with AutoGen negotiation

Your AutoGen support agent pulls historical context via `search_conversations` to help agents negotiate the best resolution path for high-priority emails. A billing agent checks account status in parallel. They debate the refund amount in their AutoGen conversation thread before updating the Front ticket. Once they reach a consensus, the support agent uses `send_inbox_reply` to deliver the agreed-upon solution to the customer using this MCP integration.

Setup guide

Set up Front 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 Front 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="Front_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

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

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

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Common questions about Front MCP in AutoGen

The AutoGen agents pass tool results, like the Front conversation details from `get_conversation_details`, through their group chat messages. This allows the next agent in the sequence to use that data to draft a reply using `send_inbox_reply`.

Yes, you can configure your AutoGen user proxy agent to intercept calls to `send_inbox_reply`. This requires a human operator to approve the drafted Front email before the server actually sends it to the customer.

You pass the tools list from `mcp_server_tools` directly into your AutoGen `AssistantAgent` constructor. The framework handles the JSON schema conversions automatically so your agents can immediately call `list_shared_inboxes` via MCP.

The MCP Server returns an error which the AutoGen agent reads. A specialized debugger agent can then run `list_inbox_teammates` to find a valid Front ID and correct the assignment using `update_conversation_status`.

All Front conversation IDs and thread metadata are processed within ephemeral, isolated V8 runtimes. Vinkius secures your Front API tokens with end-to-end encryption, ensuring that no sensitive thread details or email headers are permanently logged during AutoGen debates.

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