How to Use the FirstQuadrant MCP in Google ADK

Q: How do I set up FirstQuadrant in Google ADK?

Install the package and initialize the McpToolset with your server URL. Pass this toolset into your LlmAgent configuration to give it access to sales functions.

Q: Does FirstQuadrant support Google ADK long-context models?

Yes. You can feed the output of listcontacts and listactivities into your Gemini model. The agent will analyze your entire lead history in one pass.

Q: Can I restrict which tools FirstQuadrant exposes to my Google ADK agent?

You can use the toolnames filter during initialization. This limits your agent to only the functions you want it to perform, like createcontact.

Q: Is FirstQuadrant data compatible with BigQuery?

You can export data retrieved via listcontacts into your BigQuery tables. Many teams use this to perform deeper analysis on their outbound results.

Connect FirstQuadrant to your Google ADK pipelines. Build enterprise-grade sales agents on Google Cloud infrastructure.

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Connect FirstQuadrant MCP to Google ADK

Create your Vinkius account to connect FirstQuadrant 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 FirstQuadrant with Google ADK

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Sync FirstQuadrant with Google ADK

Use the `McpToolset` to expose sales tools to your Gemini-powered agent. Your agent gains the ability to read and write CRM data directly. Configure your environment to handle `list_properties_schema` requests. This lets your agent understand your custom CRM fields before it writes new data.

Trigger autonomous research

Your agent calls `enrich_contact_ai` to gather intelligence on every lead. It replaces the manual research process with high-speed automated lookups. Pass the results from `get_contact_details` into your LLM reasoning loop. Your agent makes better decisions when it has the full context.

Maintain pipeline velocity

Enroll leads into sequences using `add_to_campaign` as soon as they meet your criteria. Your agent handles the heavy lifting of pipeline management. Check `list_activities` periodically to ensure your agent is hitting its targets. It keeps your sales motion consistent and predictable.

Setup guide

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

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Common questions about FirstQuadrant MCP in Google ADK

Install the package and initialize the `McpToolset` with your server URL. Pass this toolset into your `LlmAgent` configuration to give it access to sales functions.

Yes. You can feed the output of `list_contacts` and `list_activities` into your Gemini model. The agent will analyze your entire lead history in one pass.

You can use the tool_names filter during initialization. This limits your agent to only the functions you want it to perform, like `create_contact`.

You can export data retrieved via `list_contacts` into your BigQuery tables. Many teams use this to perform deeper analysis on their outbound results.

We enforce strict access controls on all stored email and profile records. Your agent only sees what the tool returns during an authorized session.

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