How to Use the Moneypenny MCP in AutoGen
Deploy AutoGen agents to debate and resolve customer service escalations using live Moneypenny receptionist data.
Works with every AI agent you already use
…and any MCP-compatible client
Connect Moneypenny MCP to AutoGen
Create your Vinkius account to connect Moneypenny 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.
Consensus-driven triage of Moneypenny call logs
Let your agents debate how to handle missed calls. This MCP integration lets your agents debate whether to dispatch an immediate alert or schedule a standard follow-up after one agent pulls today's call messages using `get_today_calls` while a supervisor agent reviews the priority level. By using `list_call_messages`, your agents can compare today's messages against historical data from `get_this_month_calls`. They deliberate on whether a caller is a repeat high-value lead, ensuring your team responds to the most critical accounts first.
Multi-agent analysis of live chat logs
When chats come in, a single agent might miss the context. By feeding `get_recent_chats` and `get_today_chats` into an AutoGen group chat, a customer-success agent and a quality-assurance agent can analyze the transcript together. They can run `list_chat_logs` to audit past interactions. If one agent proposes an automated response, the other can challenge it based on the customer's historical sentiment, preventing tone-deaf automated replies.
Automated system health checks via MCP Server tools
Before triggering complex multi-agent discussions, your supervisor agent can run a quick system check. Calling `check_moneypenny_status` ensures the communication lines are open before agents attempt to fetch data. Once the status is verified, the agents pull `get_activity_summary` to review today's metrics. This quick check prevents your agents from wasting API tokens trying to analyze offline or empty communication channels.
Set up Moneypenny 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 includesmcp_server_toolsfor 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
AssistantAgentand callagent.run(). The agent invokes Moneypenny tools and returns structured results.
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="Moneypenny_assistant",
model_client=OpenAIChatCompletionClient(model="gpt-4o"),
tools=tools,
)
result = await agent.run("List recent Moneypenny data")
print(result.messages[-1].content) 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.
McpWorkbenchis 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=workbenchto your agent. State is preserved across multiple tool calls within the same session.
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="Moneypenny_assistant",
model_client=OpenAIChatCompletionClient(model="gpt-4o"),
workbench=workbench,
)
result = await agent.run("List recent Moneypenny data")
print(result.messages[-1].content) Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Moneypenny. 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 Moneypenny MCP in AutoGen
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