How to Use the Skylink MCP in AutoGen

Q: Does AutoGen handle multiple Skylink tool calls?

The structure supports it. Agents can call tools like getcall followed by listactivities, using the results to challenge or inform each other's conclusions.

Force consensus decisions on Skylink tasks using AutoGen's multi-agent debate framework.

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Setup Skylink with AutoGen

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Deliberative Lead Scoring

Instead of one agent deciding, you set up multiple agents to argue over the best follow-up path for a lead. One might call `get_lead` while another calls `list_calls`. They debate which action is optimal. The system converges on a consensus decision that moves the lead record forward.

Risk Assessment of Deals

Use specialized agents to challenge proposed deal updates. One agent might call `get_deal` for financials, while another calls `list_activities` to check history. They debate if the risk is worth the push. The final output reflects a negotiated decision, not just a simple API write.

Coordinated Contact Management

Set up agents—say, 'Data Agent' and 'Verification Agent.' The Data Agent calls `create_contact`, but the Verification Agent must call `get_account` first to validate source data. The process only completes when all competing perspectives agree on the record integrity.

Setup guide

Set up Skylink 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 Skylink 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="Skylink_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

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

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

It forces multiple specialized agents to debate and negotiate outcomes. This is useful when the correct action in Skylink isn't obvious, like deciding if a lead requires immediate task creation.

Yes. You can build pipelines where multiple agents interact with tools like `list_leads` and `create_task`, debating the optimal sequence before execution.

It gives access to all core CRM functions: deal details, contact history, lead lists, and agent performance. These records are used as evidence for the agents' debate.

The structure supports it. Agents can call tools like `get_call` followed by `list_activities`, using the results to challenge or inform each other's conclusions.

The system touches account records, contact details, deal status, and lead information. The debate process validates these different data points before any changes occur.

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