How to Use the Daktela MCP in OpenAI Agents SDK

Q: How do I connect Daktela to the OpenAI Agents SDK?

Install the OpenAI Agents SDK using pip, then initialize MCPServerStreamableHttp pointing to your Vinkius Daktela endpoint. Pass the MCP Server instance into your Agent constructor's mcpservers list to auto-discover all twelve Daktela communication tools.

Q: Can OpenAI Agents SDK handle Daktela queue routing?

Yes, your OpenAI Agents SDK agent uses listqueues to inspect live Daktela contact center queues. It then routes Daktela tickets or schedules callbacks based on real-time agent availability.

Q: How does guardrails protect Daktela data in OpenAI Agents SDK?

The OpenAI Agents SDK intercepts calls to tools like createticket before execution. You write validation logic that checks the Daktela ticket payload against your business rules before the server receives it.

Q: Does this MCP Server support tool caching?

Yes, you can set cacheToolsList=True in your OpenAI Agents SDK configuration. This prevents unnecessary network roundtrips when your agent queries the Daktela schema.

Run production-grade OpenAI Agents SDK workflows that manage Daktela tickets and CRM contacts with built-in execution guardrails.

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Connect Daktela MCP to OpenAI Agents SDK

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

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Safely manage Daktela tickets via OpenAI Agents SDK

The `create_ticket` tool lets your OpenAI agent generate support records directly inside Daktela, using the SDK's built-in guardrails to validate ticket details before execution. Your OpenAI agent uses `get_ticket` to pull real-time statuses, feeding this Daktela data into your SDK agentic handoff routines to route complex technical issues to human agents.

Trace CRM contact updates with this MCP Server

The `create_contact` tool exposes your Daktela CRM directory directly to your OpenAI Agents SDK pipeline. Every Daktela contact creation triggers tracing on your OpenAI dashboard, letting you audit how your agent handles user data. You use `list_contacts` to check existing Daktela records before your OpenAI agent creates duplicates. This keeps your Daktela database clean while your OpenAI agent runs autonomous outreach loops.

Audit communication history during agent handoffs

The `list_call_history` tool pulls raw Daktela call logs into your OpenAI agent context. This lets your OpenAI agent analyze past voice interactions before handing the customer over to a live agent. Your OpenAI agent pairs this with `list_email_history` to build a complete timeline of Daktela customer touchpoints. The OpenAI Agents SDK's built-in tracing records every Daktela API call, giving you full visibility into what the agent read.

Setup guide

Set up Daktela 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 Daktela tools at runtime.
3

Create your Agent

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

Run the agent

Call Runner.run(agent, prompt) to execute. The agent invokes the appropriate Daktela 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="Daktela Agent",
            instructions="You have access to Daktela tools.",
            mcp_servers=[server],
        )
        result = await Runner.run(agent, "List recent transactions")
        print(result.final_output)

asyncio.run(main())

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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 Daktela. 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 Daktela MCP in OpenAI Agents SDK

Install the OpenAI Agents SDK using pip, then initialize `MCPServerStreamableHttp` pointing to your Vinkius Daktela endpoint. Pass the MCP Server instance into your Agent constructor's `mcp_servers` list to auto-discover all twelve Daktela communication tools.

Yes, your OpenAI Agents SDK agent uses `list_queues` to inspect live Daktela contact center queues. It then routes Daktela tickets or schedules callbacks based on real-time agent availability.

The OpenAI Agents SDK intercepts calls to tools like `create_ticket` before execution. You write validation logic that checks the Daktela ticket payload against your business rules before the server receives it.

Yes, you can set `cacheToolsList=True` in your OpenAI Agents SDK configuration. This prevents unnecessary network roundtrips when your agent queries the Daktela schema.

Your Daktela call and email logs stay inside the Vinkius V8 sandbox during transit. The OpenAI Agents SDK accesses `list_call_history` and `list_email_history` via encrypted HTTPS, and no Daktela customer communication transcripts are stored on our servers.

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