How to Use the HighLevel MCP in Google ADK

Q: How do I restrict which HighLevel tools the Google ADK agent uses?

Use the toolnames array when configuring the MCP toolset. You can expose getcontactdetails while blocking write access entirely.

Q: Does this work with Gemini's long context?

It does. You can dump massive JSON responses from listcontacttasks straight into the prompt. Gemini handles the token load easily.

Feed HighLevel CRM data straight into Google ADK so your Gemini agents can analyze pipelines and assign tasks.

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Connect HighLevel MCP to Google ADK

Create your Vinkius account to connect HighLevel 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 HighLevel with Google ADK

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Perplexity

HighLevel MCP Server for pipelines

The agent pulls the board using `list_pipelines` to analyze your entire sales funnel at once. Gemini models have massive context windows for this exact reason. Once the data is in context, the agent cross-references it with your BigQuery tables after running `search_opportunities`. This turns a static CRM into a live data source for complex reasoning tasks.

Automated contact management

Your agent runs `search_contacts` to find prospects based on email threads. Sales reps hate logging tasks manually. After identifying the target, it executes `create_contact_task` to assign a follow-up. You can restrict the agent to just these two tools using the tool_names filter in this MCP integration.

Location and calendar ops

The agent uses `list_location_tags` to understand the regional setup before it tries to book anything. Multi-location businesses have complicated schedules. It then checks `list_calendars` and `get_calendar_free_slots` to find an opening. You get accurate scheduling across different time zones without hardcoding anything into your agent logic.

Setup guide

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

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Common questions about HighLevel MCP in Google ADK

Initialize McpToolset with StreamableHttpServerParameters pointing to your Vinkius endpoint. Pass that toolset to your LlmAgent.

Yes. The agent formats a JSON payload and sends it through create_contact. It requires at least a first name, email, or phone number.

Use the tool_names array when configuring the MCP toolset. You can expose get_contact_details while blocking write access entirely.

It does. You can dump massive JSON responses from list_contact_tasks straight into the prompt. Gemini handles the token load easily.

We run a zero-trust architecture. When your agent fetches deals via search_opportunities, the transaction happens inside a sandboxed environment that destroys itself immediately after execution. Your pipeline revenue numbers never touch our disks.

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