How to Use the Bland AI MCP in OpenAI Agents SDK

Control your phone operations directly from the OpenAI Agents SDK for production-grade voice automation.

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Connect Bland AI MCP to OpenAI Agents SDK

Create your Vinkius account to connect Bland AI 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 Bland AI with OpenAI Agents SDK

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Manage live calls with OpenAI Agents SDK

Trigger `send_call` to initiate conversations or `end_call` if you need to cut a connection immediately. Your agent handles the logic in real-time. This MCP Server provides the hooks for your SDK-based agents to maintain control. You get to decide exactly when a call drops.

Audit transcripts and audio with OpenAI Agents SDK

Use `get_call_details` to pull the exact transcript variables for any completed session. It keeps your logs clean and readable. Grab the raw audio files via `get_recording` to review specific interactions. The SDK allows you to verify what your voice agents actually said to customers.

Scale outbound operations using OpenAI Agents SDK

Fire off `send_batch` to reach a large list of numbers at once. You define the array, and the server manages the concurrent dispatch. Monitor your progress with `get_batch` to see how many calls hit their target. This setup keeps your bulk operations running without manual oversight.

Setup guide

Set up Bland AI 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 Bland AI tools at runtime.
3

Create your Agent

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

Run the agent

Call Runner.run(agent, prompt) to execute. The agent invokes the appropriate Bland AI 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="Bland AI Agent",
            instructions="You have access to Bland AI 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 Bland AI MCP in OpenAI Agents SDK

Yes, you can initiate calls directly from your code. The SDK treats these tools like local functions for your agents.

Your agent validates every tool call before execution. If the parameters for a call seem off, the SDK blocks it before it hits the network.

Absolutely. You can run multiple call requests in parallel using standard async patterns. It keeps your agent responsive while waiting for the network.

The MCP connection drops and your agent receives an error. You should implement retry logic in your Python code to handle intermittent network blips.

Your transcript logs and audio files are encrypted in transit. We store them only on the Bland AI infrastructure, and your agent only accesses them via authenticated requests.

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