How to Use the Bland AI MCP in Google ADK

Connect your Gemini enterprise agents to the Google ADK to control voice workflows.

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Connect Bland AI MCP to Google ADK

Create your Vinkius account to connect Bland AI to Google ADK 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 Google ADK

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Deploy voice agents via Google ADK

Execute `send_call` to put your Gemini agent on the phone with a lead. It bridges your cloud logic to a real-world voice conversation. Use `list_inbound` to see which numbers your agent is currently watching. It helps you keep track of your active phone infrastructure inside your Google environment.

Analyze call data using Google ADK

Run `analyze_call` to check if your agent hit its goals during a live session. You get immediate feedback on whether the conversation went well. Review the history of all interactions with `list_calls`. It lets you feed past performance data into your larger Google Cloud analytics pipelines.

Stream web sockets with Google ADK

Initiate `create_web_call` to spin up a WebRTC socket for low-latency signaling. It connects your agent to the audio stream directly. This tool gives you fine-grained control over how the signaling happens. You can manage the stream state from your ADK agent loop.

Setup guide

Set up Bland AI MCP in Google ADK

Prerequisites

Python 3.10+ installed
google-adk package (pip install google-adk)
Active Vinkius subscription with a valid endpoint token

1

Install Google ADK

Run pip install google-adk to install the Agent Development Kit. MCP support is included via the McpToolset class.
2

Connect via SSE transport

Use McpToolset.from_server() with SseServerParams pointing to your Vinkius endpoint. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.
3

Create an LlmAgent

Pass the returned mcp_tools list directly to LlmAgent(tools=mcp_tools). The ADK maps each MCP tool to a native Gemini function call — no manual schema definitions required.
4

Run with any Gemini model

The agent works with any Gemini model (gemini-2.0-flash, gemini-2.5-pro, etc.). Copy the full example on the right to get started with Bland AI tools in your ADK agent.

agent.py

from google.adk.agents import LlmAgent
from google.adk.tools.mcp_tool.mcp_toolset import McpToolset
from google.adk.tools.mcp_tool.mcp_session_manager import SseServerParams

# Connect to the MCP via SSE
mcp_tools, exit_stack = await McpToolset.from_server(
    connection_params=SseServerParams(
        url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
    )
)

# Create your agent with auto-discovered tools
agent = LlmAgent(
    name="Bland AI_agent",
    model="gemini-2.0-flash",
    instruction="You have access to Bland AI tools via MCP.",
    tools=mcp_tools,
)

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Common questions about Bland AI MCP in Google ADK

It does. You can import the tools into your ADK project and start calling functions immediately.

Yes, you can pull batch aggregations and long-form transcripts. The Gemini models handle the reasoning over this data easily.

The server runs externally, so your agent remains fast. It only calls the server when you explicitly invoke a tool.

You pass a list of names to the McpToolset constructor. This hides any functions you don't want your agent to touch.

Data is protected by TLS during transmission. We isolate every customer's transcript and audio files, ensuring they remain private and accessible only via your secure token.

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