How to Use the Bland AI MCP in AutoGen

Let your AutoGen agents debate and execute Bland AI phone campaigns through consensus-driven decision making.

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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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Debate Bland AI calls in AutoGen

Assign one agent to monitor call costs while another agent triggers `send_batch`. They discuss the plan in AutoGen before any calls are actually placed. It prevents mistakes. If the performance agent wants to scale up, the security agent can challenge that decision based on current limits.

Analyze performance with AutoGen

Have your agents review `get_batch` data together. They interpret the results and decide if the current batch needs to be adjusted or stopped. This makes your operations smarter. The agents negotiate based on the actual status of the calls, ensuring they reach the target goals.

Manage inbound numbers via AutoGen

Use `list_inbound` to let your agents see available phone lines. They decide which agent gets assigned to which number based on the current workload. It is a collaborative effort. The agents coordinate the entire telecom array, keeping everything organized without manual input.

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 →

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Common questions about Bland AI MCP in AutoGen

Yes, agents debate the execution plan before triggering tools like `send_call`. This ensures that every call is vetted by your system's logic.

You pass the tools to the AssistantAgent constructor after configuring the server. The adapter handles the schema so your agents can start talking to the API immediately.

They do, as each tool call is just another action for the agents to discuss. They can challenge or approve the use of any tool in the set.

It supports both stdio and Streamable HTTP. You choose the one that fits your deployment environment best.

Call metadata is treated as a private resource. We use strict token-based authentication to ensure that only your specific agent group can pull or modify call variables.

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