How to Use the Agify MCP in AutoGen

Q: How do I register the Agify MCP Server in AutoGen?

Install the autogen-extmcp package. Initialize mcpservertools with your Vinkius URL and pass the resulting list directly to your AssistantAgent.

Q: Can AutoGen handle the batch prediction tool?

Yes. The McpToolAdapter automatically converts the batch schema. Your agent knows it can send an array of strings instead of calling the single-name tool repeatedly.

Give your AutoGen agents the ability to debate demographic data using the Agify MCP server.

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Connect Agify MCP to AutoGen

Create your Vinkius account to connect Agify 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 Agify with AutoGen

Ask AI about this MCP

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Claude

Perplexity

Fuel AutoGen Debates with Agify

The `predict_age` tool injects hard demographic estimates into your AutoGen chats. A marketing agent pulls the age prediction for a target customer's name. A separate risk-analysis agent reviews that same number. They argue over whether the predicted age fits the target demographic, using the tool's output as the factual baseline for their negotiation.

Process Group Demographics Fast

Analyzing a whole committee requires the `predict_age_batch` tool. One agent compiles the list of board members and fires the array to the MCP Server in a single turn. The entire conversation immediately has access to the group's estimated age spread. The agents can then deliberate on whether the board skews too old or too young based on the batch results.

Argue Regional Name Variations

Geographic context changes everything in a multi-agent setup. The `predict_age` tool accepts a country code, allowing agents to test different regional assumptions. An agent analyzing a global database might pull the age for "Maria" in Spain, while another checks "Maria" in Mexico. They compare the results to decide which market requires a different advertising tone.

Setup guide

Set up Agify 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 Agify 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="Agify_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

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

Get your connection token →

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="Agify_assistant",
        model_client=OpenAIChatCompletionClient(model="gpt-4o"),
        workbench=workbench,
    )

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

Install the `autogen-ext[mcp]` package. Initialize `mcp_server_tools` with your Vinkius URL and pass the resulting list directly to your AssistantAgent.

Assign it to a dedicated data-gathering agent. That agent fetches the predictions and reports back to the main chat so the reasoning agents can debate the results.

Yes. The `McpToolAdapter` automatically converts the batch schema. Your agent knows it can send an array of strings instead of calling the single-name tool repeatedly.

That is the point of the framework. The tool provides a statistical estimate, and your agents deliberate on how much weight to give that number in their final decision.

Vinkius operates on a zero-trust model. First names and country parameters are routed through a sandboxed environment that drops all network connections except the target API, logging nothing.

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