How to Use the Skalin MCP in AutoGen

Q: What if I need to list customer accounts with AutoGen?

You can assign one agent the task of calling listcsaccounts. A second agent then reviews that list, challenging its completeness or structure before presenting it as a final result.

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Setup Skalin with AutoGen

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Debating Account Status Changes

Set up a debate between two agents. Agent A calls `get_account_health` to report the current status. Agent B, the risk assessor, then calls `list_cs_alerts` and challenges Agent A's conclusion based on potential risks. The conversation converges only when both parties agree on the next action.

Negotiating Task Updates

A multi-agent system can draft a response. One agent uses `list_account_contacts` to find the right person, and another uses that list to determine which success task needs updating via `update_cs_task`. They debate who owns the communication before finalizing the action. The final output is an agreed-upon, validated plan.

Consensus on Account Setup

Need to provision a new account? One agent drafts the request using `create_cs_account`. A second agent verifies all necessary contacts by calling `list_account_contacts` for validation. They argue over whether enough data was provided before approving the final record. The system forces deliberation, preventing incomplete setups.

Setup guide

Set up Skalin 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 Skalin 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="Skalin_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

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

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

You set up agents to debate the best approach. One agent may call `list_account_interactions` to gather data, and a second agent challenges that data's completeness before summarizing it.

Yes. You can mandate a debate where one agent gathers inputs using `get_account_metrics`, and another agent cross-references those numbers against available alerts from `list_cs_alerts` to validate the findings.

You can assign one agent the task of calling `list_cs_accounts`. A second agent then reviews that list, challenging its completeness or structure before presenting it as a final result.

The server exposes sensitive account and contact information. The multi-agent framework ensures that multiple viewpoints review the necessary data points before any action is taken.

You can set up a process where one agent drafts a meeting summary, another agent reviews it for tone and accuracy (using `log_interaction`), and finally, the system commits the record only after consensus.

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