How to Use the Bland AI MCP in AutoGen

Deploy a multi-agent team with AutoGen to debate and execute Bland AI voice tasks.

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Connect Bland AI MCP to AutoGen

Create your Vinkius account to connect Bland AI to AutoGen 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 AutoGen

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Perplexity

Coordinate Bland AI agents via AutoGen

Let your agents debate whether to `send_phone_call` based on user input. One agent can check availability while another plans the call content. This consensus model prevents accidental calls. Your agents check each other's work before invoking any tool.

Analyze call data using AutoGen agents

Assign a research agent to call `list_recent_calls` and summarize the results. Another agent can then interpret the data for your team. They work together to turn raw logs into actionable reports. You get the benefit of multiple perspectives on every conversation.

Configure voice settings with AutoGen

Use `update_agent_config` as part of your team's workflow. If a sales agent needs a new persona, your team can negotiate the change programmatically. This keeps your agent configurations dynamic. Your system adapts to new requirements without manual intervention.

Setup guide

Set up Bland AI 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 Bland AI 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="Bland AI_assistant",
    model_client=OpenAIChatCompletionClient(model="gpt-4o"),
    tools=tools,
)

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

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

They can. By giving your agents access to the server, they can discuss which tool to use. This is useful for complex decision-making tasks.

You pass the tool list to your AssistantAgent constructor. This makes the server tools available to all participating agents in your group chat.

It functions perfectly within the framework. Your agents can call tools to fetch data or trigger actions while maintaining their internal dialogue.

The agents keep debating until they reach a consensus. You can define a supervisor agent to make the final call if they get stuck.

The server uses a dedicated endpoint token for every session. Your call metadata and transcripts are restricted to the agents you authorize.

Use it with your favorite AI tools

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

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Google ADK sdk-python

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Mastra AI sdk-typescript

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