How to Use the Goodcall MCP in Google ADK

Q: How do I link Goodcall to Google ADK?

Use the McpToolset class in your Python code. Provide the endpoint URL, and your Google ADK agent will discover all 13 tools instantly.

Q: Can I monitor Goodcall health from Google ADK?

Absolutely. Include checkgoodcallstatus in your health check routine. It returns a simple status code to confirm your connection is active.

Q: How does Google ADK handle Goodcall call summaries?

Your agent calls getcallsummary and receives a structured text response. You can then pipe that directly into your Vertex AI workflow.

Integrate Goodcall with Google ADK. Build enterprise-grade receptionist agents that act on your BigQuery data.

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Connect Goodcall MCP to Google ADK

Create your Vinkius account to connect Goodcall 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 Goodcall with Google ADK

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Google ADK and Goodcall server connectivity

Connect your Gemini-powered agent to your phone system using the `McpToolset`. It maps the 13 Goodcall tools directly into your Google ADK environment. This setup allows your agent to fetch `list_faqs` or `get_call_summary` while keeping all your logic within the Google Cloud ecosystem.

Manage your receptionist via Google ADK

Control your business phone presence from your terminal. Use `list_agents` to see your current setup and `update_agent` to refine your greeting instructions. Your agent can now handle complex receptionist tasks because it has direct access to your live call logs. No manual entry required.

Actionable call data for your Google ADK agent

Feed `list_calls` output into your BigQuery pipelines. Your agent can query your history to find patterns in customer inquiries. This creates a closed loop where phone activity informs your business decisions. It turns raw audio into structured, searchable records.

Setup guide

Set up Goodcall 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 Goodcall 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="Goodcall_agent",
    model="gemini-2.0-flash",
    instruction="You have access to Goodcall tools via MCP.",
    tools=mcp_tools,
)

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Common questions about Goodcall MCP in Google ADK

Use the `McpToolset` class in your Python code. Provide the endpoint URL, and your Google ADK agent will discover all 13 tools instantly.

Yes, you can expose all tools or use a filter. If you only need `list_bookings` and `get_transcript`, you can restrict the agent's access accordingly.

Goodcall transmits data over encrypted tunnels. Your Google ADK environment handles the records locally, and we never touch your raw customer information.

Absolutely. Include `check_goodcall_status` in your health check routine. It returns a simple status code to confirm your connection is active.

Your agent calls `get_call_summary` and receives a structured text response. You can then pipe that directly into your Vertex AI workflow.

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