How to Use the Mattermark MCP in Google ADK

Q: How does the Google ADK interact with the Mattermark tools?

You use the McpToolset class to register the server. Your agent then gains access to all 10 tools as native function calls.

Bring startup data into your Google ADK infrastructure and build intelligent agents on Google Cloud.

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Connect Mattermark MCP to Google ADK

Create your Vinkius account to connect Mattermark 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 Mattermark with Google ADK

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Enterprise data integration with Google ADK

Connect your agents to the Mattermark data stream to enrich your BigQuery datasets. Your agents use `search_funding_rounds` to pull historical data into your Google Cloud environment for long-context reasoning. Gemini models handle the heavy lifting. By providing `get_company_employees` data, you give your agents the context they need to analyze organizational growth patterns at scale.

Scale research with Gemini context

Leverage the 1M+ token window to ingest massive amounts of data from `get_company_news` and `get_company_details`. Your agents process this information to generate summaries of market trends. This setup bridges the gap between raw API data and enterprise intelligence. You get direct results without leaving your Google ADK workflow.

Granular agent tool control

Restrict which tools your agents can use by applying a tool name filter. If you only need `list_investors` and `get_investor_details`, you can exclude the rest to keep your agent’s decision space clean. This control ensures your agents stay focused on their specific tasks. It’s a clean way to manage complex research pipelines within your existing infrastructure.

Setup guide

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

Get your connection token →

Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Mattermark. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.

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Common questions about Mattermark MCP in Google ADK

You use the `McpToolset` class to register the server. Your agent then gains access to all 10 tools as native function calls.

You can fetch data using these tools and write the output into your tables. The agent acts as the bridge between the API and your data warehouse.

Yes, the transport is configurable. Choose the one that fits your deployment architecture best.

Data privacy is maintained through your endpoint token. Your proprietary startup intelligence never leaves your secure Google Cloud environment.

The Gemini models are built for exactly that. They parse the structured output from these tools efficiently, even with large datasets.

Use it with your favorite AI tools

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

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