How to Use the Bland AI MCP in OpenAI Agents SDK

Deploy voice agents directly from your OpenAI Agents SDK codebase to manage outbound calls and transcript analysis.

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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 voice personas via OpenAI Agents SDK

You can spin up or tear down `create_voice_agent` and `delete_voice_agent` calls right inside your Python logic. This keeps your agent registry in sync with your production environment without switching tabs. Updating settings is just as fast. You use `update_agent_config` to adjust how your agent behaves, ensuring your OpenAI Agents SDK setup stays current with your business needs.

Automate phone interactions with this MCP Server

Trigger `send_phone_call` to initiate outbound reach-outs. Your agent handles the dial, and you monitor the progress using `list_recent_calls` directly from your script. Stopping a conversation is immediate. If a call goes off the rails, `stop_active_call` kills the connection before your agent keeps talking.

Review call performance and data

Pull raw data using `get_call_details` to feed your analytics pipeline. This MCP Server lets you see exactly what happened on every interaction. Run `analyze_call_transcript` to get the breakdown of a conversation. It turns raw audio logs into structured feedback for your team.

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

Use the MCPServerStreamableHttp class to point your agent at the Vinkius endpoint. This lets your code see all twelve tools as native functions.

Yes, just call the listing tools to pull call history into your agent's memory. It processes the text files locally in your Python environment.

Absolutely, the agent framework handles the tool calls automatically. You define the logic, and the server executes the phone actions.

The server returns an error code you can catch in your Python try-except block. It stops the agent from guessing why the call dropped.

Vinkius isolates your traffic in a private sandbox. Your voice transcripts never hit shared infrastructure during the analysis process.

Use it with your favorite AI tools

Connect this server to Cursor, Claude, VS Code, and more.

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