How to Use the Kavkom MCP in OpenAI Agents SDK

Give your OpenAI Agents SDK production environment direct control over your Kavkom phone system and contact records.

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Connect Kavkom MCP to OpenAI Agents SDK

Create your Vinkius account to connect Kavkom to OpenAI Agents SDK 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 Kavkom with OpenAI Agents SDK

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Claude

Perplexity

Manage contacts and calls in Python

Your agent uses `create_contact` to save new leads directly into your database. It handles the record creation while you focus on the conversation flow. Running `list_crm_contacts` lets the agent verify existing records before making outbound calls. This keeps your data clean without manual entry.

Automate SMS and call history tracking

The agent sends messages using `send_sms_message` to follow up with customers immediately after a call ends. It handles the request asynchronously to keep your agent loop fast. Use `list_sms_history` to pull recent logs for agent review. This gives your system the context it needs to stay informed on previous customer interactions.

Synchronize team and call data

Invoke `list_team_members` so your agent knows exactly who is available to handle a transfer. It maps your internal staff list to current call routing needs. Calling `get_call_details` provides metadata on specific sessions. Your agent uses this to generate summaries or log call quality metrics for your dashboard.

Setup guide

Set up Kavkom MCP in OpenAI Agents SDK

Prerequisites

Python 3.10+ installed
openai-agents package (pip install openai-agents)
Active Vinkius subscription with a valid endpoint token

1

Install the SDK

Run pip install openai-agents to install the OpenAI Agents SDK. The MCP integration is built-in — no extra dependencies needed.
2

Connect via SSE transport

Use MCPServerSse with your Vinkius endpoint URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. The SDK auto-discovers all Kavkom tools at runtime.
3

Create your Agent

Pass the MCP to Agent(mcp_servers=[server]). The agent receives Kavkom tools as native definitions — JSON schemas resolve automatically.
4

Run the agent

Call Runner.run(agent, prompt) to execute. The agent invokes the appropriate Kavkom tools and returns structured results. Copy the full example on the right to get started.

agent.py

import asyncio
from agents import Agent, Runner
from agents.mcp import MCPServerSse

async def main():
    async with MCPServerSse(
        url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
    ) as server:
        agent = Agent(
            name="Kavkom Agent",
            instructions="You have access to Kavkom tools.",
            mcp_servers=[server],
        )
        result = await Runner.run(agent, "List recent transactions")
        print(result.final_output)

asyncio.run(main())

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Common questions about Kavkom MCP in OpenAI Agents SDK

You instantiate an MCPServerStreamableHttp object pointing to your Kavkom endpoint. The SDK automatically discovers all 7 tools, allowing you to pass them into your agent constructor via the mcp_servers parameter.

Yes. Since you are building a production system, use the SDK built-in validation to check tool arguments before execution. This prevents the agent from sending malformed SMS or creating duplicate contacts.

Your agent interacts with Kavkom via an ephemeral connection that requires an API token. The server operates in a zero-trust sandbox, ensuring that only your authorized agent can access call logs or contact info.

Yes. Because you are using the OpenAI Agents SDK, every tool call is traced through the standard dashboard. You can monitor exactly when `send_sms_message` or `list_calls` is triggered.

The server returns an error which the agent handles via standard Python exceptions. You should implement a retry block in your agent logic to manage transient network issues.

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