How to Use the DeckMatch MCP in Google ADK

Q: How do I monitor tool usage in Google ADK?

Use the toolnames filter to restrict which functions your agent calls. You can log these calls to track performance and data access.

Connect your Google ADK agents to startup data and automate your investment workflow.

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Connect DeckMatch MCP to Google ADK

Create your Vinkius account to connect DeckMatch 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 DeckMatch with Google ADK

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Sync Google ADK with startup submissions

Call `list_submissions` to fetch all analyzed decks into your Google Cloud environment. Your agents can then reason over the entire dataset at once. Use `tag_submission` to organize these records by sector or stage. It keeps your pipeline structured for better retrieval later.

Manage outreach via Google ADK tools

Trigger `submit_pitch_deck` to pipe files from your cloud storage directly into the analysis engine. It bypasses the need for manual file handling. Run `list_submission_tags` to see how your agent has categorized current leads. This helps you monitor the status of your funnel in real-time.

Improve reasoning with an MCP Server

Invoke `search_startups_semantically` to pull similar profiles based on your current context. Your agent uses this to find relevant connections across your entire dataset. Use `generate_investment_memo` to draft summaries for your team. This MCP server turns raw data into actionable insights for your agents.

Setup guide

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

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Common questions about DeckMatch MCP in Google ADK

Yes, you connect it using the McpToolset class. It supports both Stdio and HTTP transports for your enterprise agents.

Vinkius uses zero-trust principles to isolate your data. The server only exposes what you explicitly allow, keeping your pitch deck info secure.

You can pass data from BigQuery into the submission tools. The agent acts as the bridge between your cloud warehouse and the analysis engine.

Use the tool_names filter to restrict which functions your agent calls. You can log these calls to track performance and data access.

Your startup pitch decks are processed in an ephemeral sandbox. No data persists longer than the required analysis window.

Use it with your favorite AI tools

Connect this server to Cursor, Claude, VS Code, and more.

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Google ADK sdk-python

Pydantic AI sdk-python

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