How to Use the Adikteev MCP in AutoGen

Q: How do I give my AutoGen agents access to Adikteev tools?

Install the autogen-extmcp package first. Then, use the mcpservertools helper function with your Vinkius server URL. This returns a list of tools you can pass directly to your AssistantAgent constructor.

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Connect Adikteev MCP to AutoGen

Create your Vinkius account to connect Adikteev 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 Adikteev with AutoGen

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Multi-Agent Campaign Strategy

Give Adikteev tools to a group of specialized AutoGen agents and watch them collaborate. For instance, a 'MarketingStrategist' agent could propose a new audience by describing a segment. A 'TechnicalMarketer' agent would then translate that into a call to the `create_segment` tool. A 'FinanceAgent' in the same conversation could use `get_reporting` to pull performance data from a similar past campaign and argue whether the new segment is a good investment. This isn't a single command; it's a conversation that leads to a smarter decision.

Automated Churn Analysis and Response

You can build an autonomous team to monitor and act on churn signals. A 'ChurnMonitor' agent could periodically run `get_churn_scores`. If it spots a spike in at-risk users, it can start a conversation with a 'CampaignManager' agent. They would discuss the problem, using the Adikteev tools to gather more data. The 'CampaignManager' might decide to create a new retargeting segment for those users and kick off a campaign, with a 'User' proxy agent asking for your final approval before executing.

Adikteev Tools for Your AutoGen Team

This MCP server makes it easy to equip your AutoGen agents with Adikteev capabilities. The provided helper function, `mcp_server_tools`, fetches the tool schemas and makes them available to any agent in your group chat. The `McpToolAdapter` handles the work of translating agent requests into valid API calls to Adikteev. You just give the tools to your agents, and they can start using them as part of their conversational problem-solving process.

Setup guide

Set up Adikteev 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 Adikteev 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="Adikteev_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

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

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

Install the `autogen-ext[mcp]` package first. Then, use the `mcp_server_tools` helper function with your Vinkius server URL. This returns a list of tools you can pass directly to your `AssistantAgent` constructor.

Yes. You can create multiple agents and assign the full tool list to the group chat manager. Alternatively, you can give specific tools to specific agents, like a 'ReportingAgent' that can only call `get_reporting`, to enforce separation of duties.

The agents' underlying models decide based on the conversation history and their specific instructions. If one agent says, "We need to find out who is about to churn," another agent will likely recognize that the `get_churn_scores` tool is the right one for the job.

No. AutoGen is built for human-in-the-loop workflows. You can set up a `UserProxyAgent` that requires your explicit approval before an agent can execute a tool like `create_segment`, so you always have final say.

Your data, like churn scores and campaign metrics, is passed as text within the agent conversation. The Vinkius MCP server itself is stateless and just executes the tool calls. You are in control of the environment where your AutoGen agents are running, so the security of the conversation data is up to you.

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