How to Use the Kavkom MCP in AutoGen

Q: How do I initialize the Kavkom MCP Server in AutoGen?

Install autogen-extmcp. Pass your server URL to StreamableHttpServerParams and fetch the tools. Provide that list to your AssistantAgent constructor.

Q: Can multiple AutoGen agents use Kavkom MCP tools simultaneously?

You assign specific tools to specific agents. Give listsmshistory to your reader agent and sendsmsmessage to your writer agent. They coordinate the read/write process through conversation.

Q: Does AutoGen support Kavkom team member lookups?

Yes. Any agent equipped with the listteammembers tool can map out your account structure. They use this context to route tasks or summarize activity.

Let AutoGen agents debate and manage your Kavkom cloud telephony workflows autonomously via MCP.

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Setup Kavkom with AutoGen

Ask AI about this MCP

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Perplexity

Multi-Agent Telephony Routing

One agent shouldn't make every decision. You set up a system where a support agent retrieves logs via `list_calls`. A separate QA agent reviews those logs for missed connections. They debate the priority of each dropped call. Once they reach a consensus, a third agent executes `send_sms_message` to text the highest-value customers first. The workload is distributed and verified.

Autonomous CRM Management

Keeping contacts updated is tedious work. Your AutoGen system can dedicate a specific agent to run `list_crm_contacts` every morning. It checks for missing details and flags them to a manager agent. If the manager approves, the execution agent runs `create_contact` to fix the missing records. The agents communicate with each other until the database matches your actual call history.

Kavkom MCP Server Activity Audits

Evaluating European support teams requires context. You assign an analytics agent to pull user data with `list_team_members`. A compliance agent then pulls the corresponding `get_call_details` for those users. The two agents cross-reference the data. If the compliance agent spots an anomaly in call duration, it challenges the analytics agent. They present a joint, verified report to you in the terminal.

Setup guide

Set up Kavkom 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 Kavkom 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="Kavkom_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

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

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

Install autogen-ext[mcp]. Pass your server URL to StreamableHttpServerParams and fetch the tools. Provide that list to your AssistantAgent constructor.

You assign specific tools to specific agents. Give list_sms_history to your reader agent and send_sms_message to your writer agent. They coordinate the read/write process through conversation.

That is the framework working as intended. If an agent pulls vague data from get_call_details, a peer agent will demand clarification before allowing a text message to go out.

Yes. Any agent equipped with the list_team_members tool can map out your account structure. They use this context to route tasks or summarize activity.

Agents process raw text message contents and caller IDs in plain text. This data lives in the conversation history of your agent group. If you log the chat, you log the sensitive data, so configure your storage carefully.

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