How to Use the Numbers API MCP in Google ADK

Q: How do I restrict which Numbers API tools are visible to my Google ADK agent?

Use the toolnames filter when initializing your McpToolset in Python. This lets you expose only getmathfact while hiding trivia tools if your enterprise agent only needs mathematical data.

Feed historical and mathematical data into your Gemini models using the Numbers API and Google ADK.

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Connect Numbers API MCP to Google ADK

Create your Vinkius account to connect Numbers API 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 Numbers API with Google ADK

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Enterprise Math Queries for Google ADK

The `get_math_fact` tool retrieves specific mathematical properties that Gemini can use to analyze structured data. When your Google ADK agent processes numeric columns from BigQuery, it calls this tool to understand the mathematical significance of those values. This MCP Server fits directly into your Vertex AI pipelines. By exposing these mathematical details, your agent makes better decisions when sorting or classifying numerical datasets.

Long-Context Historical Reasoning

The `get_year_fact` and `get_date_fact` tools supply historical milestones that Gemini processes within its million-token context window. Your agent pulls centuries of date facts to build timelines or verify historical references in your enterprise documents. Our managed MCP Server runs on a secure V8 sandbox, ensuring your queries execute with sub-200ms latency. The agent uses standard HTTP transports to fetch these facts, keeping your cloud infrastructure clean.

Unstructured Trivia Ingestion

The `get_trivia_fact` and `get_random_fact` tools provide raw trivia that helps your agent generate engaging copy or verify general knowledge. You can filter these tools using the `tool_names` parameter in your toolset configuration to control what Gemini can access. This control prevents your agent from drifting into irrelevant facts during precise tasks. You decide exactly when Gemini should rely on external trivia versus internal enterprise data.

Setup guide

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

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Common questions about Numbers API MCP in Google ADK

Use the `tool_names` filter when initializing your `McpToolset` in Python. This lets you expose only `get_math_fact` while hiding trivia tools if your enterprise agent only needs mathematical data.

Yes, your agent can read rows from BigQuery and immediately pass those numbers to `get_trivia_fact` or `get_year_fact`. This lets Gemini enrich your structured database tables with real-world historical context.

This managed MCP server supports both Stdio and HTTP transports. For cloud-deployed Gemini agents on Vertex AI, the HTTP transport provides the most reliable connection.

Gemini natively parses the raw text outputs from tools like `get_date_fact` into its long-context memory. It uses this information to answer complex user queries without needing structured JSON parsing.

We run every tool execution in an isolated, ephemeral sandbox that handles only the specific numbers or dates you send. No database credentials or Gemini context payloads are ever exposed or stored by the server.

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