How to Use the Better Proposals MCP in Google ADK

Q: How do I initialize the Better Proposals MCP Server in Google ADK?

Install the ADK and use McpToolset with your Vinkius HTTP URL. Pass this toolset to your LlmAgent to instantly expose all ten document management tools to Gemini.

Connect Gemini models to your document pipeline using the Google ADK to analyze and generate sales agreements.

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Connect Better Proposals MCP to Google ADK

Create your Vinkius account to connect Better Proposals 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 Better Proposals with Google ADK

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Generate proposals via Google ADK workflows

`create_proposal` lets your Gemini-powered agent write and send agreements directly from Python. It references your existing templates using `list_templates` to keep branding consistent. Because Gemini handles massive context windows, you can pass entire BigQuery customer profiles into the Google ADK. The agent reads the history and builds a highly targeted proposal in one run.

Keep contact lists synced with BigQuery data

`create_contact` writes new client profiles directly into your sales pipeline database. Your agent verifies existing records first with `list_contacts` and `get_contact` to prevent duplicates. This integration bridges the gap between your cloud warehouse and your sales tool. You can run batch updates from Vertex AI that automatically clean up contact records.

Pull performance data into your enterprise MCP Server

`get_analytics` retrieves detailed engagement metrics for your active deals. The agent sees when a client opened a proposal and which sections they read. This MCP Server runs inside a zero-trust environment on Vinkius. Your enterprise data stays isolated, and you only need a single token to authenticate all Gemini tool calls.

Setup guide

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

Get your connection token →

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Common questions about Better Proposals MCP in Google ADK

Install the ADK and use `McpToolset` with your Vinkius HTTP URL. Pass this toolset to your `LlmAgent` to instantly expose all ten document management tools to Gemini.

You can pass an optional tool name filter to the toolset during initialization. This prevents the agent from calling destructive or sensitive tools if you only want it reading data.

Gemini's million-token context window allows the agent to analyze hundreds of previous proposals returned by `list_proposals` to write better pitches.

The server supports both transport methods. For cloud deployments on Google Cloud Run, we recommend the HTTP transport for better scalability.

All transactions go through an ephemeral, isolated environment that destroys session data immediately after execution. Your sensitive client emails and phone numbers never persist on our infrastructure.

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