How to Use the Goodcall MCP in AutoGen

Debate your business strategy in AutoGen by having agents negotiate call analytics and receptionist updates.

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Create your Vinkius account to connect Goodcall 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 Goodcall with AutoGen

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Perplexity

Consensus-driven receptionist management in AutoGen

Set up a team of agents that argue over how to handle incoming traffic. One agent pulls data using `get_analytics` while another proposes changes to `update_agent` based on the findings. This prevents hasty decisions. The agents check each other's work, ensuring that your receptionist settings align with the actual volume of missed calls.

Resolve scheduling conflicts with multi-agent debate

Use multiple agents to verify bookings retrieved via `list_bookings`. If one agent finds a conflict, it can challenge the other to find a better slot. It brings a human-like deliberation to your calendar. You get a final decision that has been vetted by competing perspectives, not just a blind automated action.

Rapid triage of missed calls

Automate the follow-up process by letting agents review `list_missed_calls` together. They can debate which callers need immediate attention versus which can be handled later. It turns a list of names into an action plan. Your agents negotiate the priority, ensuring your team knows exactly who to call back first.

Setup guide

Set up Goodcall 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 Goodcall 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="Goodcall_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

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

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

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Common questions about Goodcall MCP in AutoGen

Pass the server tools into your agent constructor. The adapter will handle the schema conversion, allowing your agents to start calling the API immediately.

Yes, the agents operate in a shared conversation state. Once one agent fetches a transcript, the others can discuss it to form a conclusion.

The server maintains strict access control. Only the agents you authorize can see the call logs or transcript contents during their negotiation.

The tools are built for speed. If you need a sanity check, have an agent call `check_goodcall_status` before starting any long-running debate.

The server exposes call records, transcripts, and booking data. This is the exact information your agents need to negotiate your business operations.

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