How to Use the vCard Contacts Parser Extended MCP in AutoGen

Q: If I use vCard Contacts Parser Extended with AutoGen, how do I manage the file path?

You provide the absolute file path to the .vcf file. The tool handles the parsing and outputting of the structured JSON that feeds into your agent's conversation loop.

Feed structured contact lists into your AutoGen debate so agents can decide who to talk to next.

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Connect vCard Contacts Parser Extended MCP to AutoGen

Create your Vinkius account to connect vCard Contacts Parser Extended 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 vCard Contacts Parser Extended with AutoGen

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Input for Agent Debate and Deliberation

The `parse_vcard_contacts` tool converts messy vCards into clean JSON, which becomes the primary input for your AutoGen agents. Instead of arguing about raw files, they debate based on structured data. You feed the contact list to the system, and then specialized agents—like a 'Scheduling Agent' or 'Risk Agent'—can challenge each other's conclusions regarding who should be contacted first.

Multi-Agent Review of Contact Lists

You can set up multiple autonomous agents to review the contacts. One agent might check for conflicting email formats, while another verifies organizational data pulled by `parse_vcard_contacts`. They negotiate and converge on a definitive list. This consensus-driven approach ensures your final contact list is vetted from several operational perspectives.

Contextualizing Conversations with Contacts

Need agents to discuss who they should talk to? The JSON output provides the context. Agents use the names, phones, and emails extracted by `parse_vcard_contacts` to determine the most logical next step in a complex conversation. It moves your system beyond simple execution; it enables true decision-making among competing perspectives.

Setup guide

Set up vCard Contacts Parser Extended 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 vCard Contacts Parser Extended 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="vCard Contacts Parser Extended_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

result = await agent.run("List recent vCard Contacts Parser Extended 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="vCard Contacts Parser Extended_assistant",
        model_client=OpenAIChatCompletionClient(model="gpt-4o"),
        workbench=workbench,
    )

    result = await agent.run("List recent vCard Contacts Parser Extended data")
    print(result.messages[-1].content)

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Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by vcard-parser. 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 vCard Contacts Parser Extended MCP in AutoGen

The tool's JSON output acts as the initial, structured context for your multi-agent system. Agents use this clean list of contacts to start their debate and decision process.

Yes. By providing highly accurate, structured data about people (names, emails), you give the agents better information to work with, improving their ability to make informed decisions.

It provides names, phone numbers, emails, and organization details. This structured JSON is ideal for agents that need factual input to deliberate on a course of action.

You provide the absolute file path to the `.vcf` file. The tool handles the parsing and outputting of the structured JSON that feeds into your agent's conversation loop.

This server touches personal contact details (names, emails, phone numbers). When building multi-agent pipelines, ensure that the debate and resulting decisions respect all applicable data governance rules.

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