How to Use the FatSecret MCP in Google ADK

Q: Can my Google ADK agent write FatSecret search results directly to BigQuery?

Yes, your agent can call searchfatsecretfoods and format the output. You then write a custom tool in your ADK setup to save that structured JSON to your BigQuery tables.

Run Gemini agents on Google ADK to analyze millions of FatSecret food items and stream calorie logs directly into your BigQuery tables.

See Vinkius in Action

Works with every AI agent you already use

…and any MCP-compatible client

MCP Servers - Free for Subscribers

Connect FatSecret MCP to Google ADK

Create your Vinkius account to connect FatSecret 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.

GDPR Free for Subscribers

Setup FatSecret with Google ADK

Ask AI about this MCP

ChatGPT

Claude

Perplexity

Analyze FatSecret foods using Google ADK

The `search_fatsecret_foods` tool pulls raw calorie and macronutrient data directly into your Google ADK agent execution context. This allows your agent to run search queries and immediately pipe the structured JSON results into BigQuery for long-term cohort analysis. You pass the MCP Server HTTP parameters to the toolset constructor to expose the database. Gemini models use their long-context windows to compare thousands of search results in a single run.

Extract detailed macro facts for Vertex pipelines

The `get_fatsecret_food_details` tool provides detailed serving-by-serving nutritional facts for any food item. Your Google ADK agent uses these precise metrics to feed enterprise recommendation models or health tracking dashboards. By integrating this tool with your Vertex AI workflows, you bypass custom API integration code using this MCP setup. The agent reads the FatSecret schema, calls the tool, and outputs structured nutrition data.

Control food database tool exposure

The `search_fatsecret_foods` tool can be isolated or combined with other enterprise tools using the ADK toolset filter. This ensures your Gemini agent only interacts with the FatSecret database when processing diet-related queries. You configure the transport layer via standard HTTP or Stdio. This flexibility lets you run the agent securely within your Google Cloud VPC.

Setup guide

Set up FatSecret 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 FatSecret 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="FatSecret_agent",
    model="gemini-2.0-flash",
    instruction="You have access to FatSecret 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 FatSecret. 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.

Why Choose Vinkius

Vinkius connects your tools to AI with real-time monitoring and automatic cost savings — all from one dashboard.

Connect FatSecret now

Real-time monitoring

Live

visibility into every interaction

Connect your favorite tools to your AI and see exactly what's happening — every request, every response, in real time.

Built-in savings

60%

lower AI costs

Vinkius compresses data between your apps and your AI automatically. Lower bills every month — no configuration required.

Single dashboard

One

place for every integration

Every tool your AI connects to, managed from a single screen. One account, complete control.

Common questions about FatSecret MCP in Google ADK

You use the McpToolset class with the Vinkius HTTP URL. Pass this toolset directly to your LlmAgent constructor to expose the food search capabilities.

Yes, your agent can call `search_fatsecret_foods` and format the output. You then write a custom tool in your ADK setup to save that structured JSON to your BigQuery tables.

Yes, the framework supports both transports. For most production cloud deployments on Google Cloud, the HTTP transport is the most reliable way to connect.

Gemini models use their large context windows to parse all serving sizes returned by `get_fatsecret_food_details`. The model easily converts between grams, ounces, and cups based on user requests.

Every request runs in an isolated, zero-trust sandbox. Your diet queries and macro logs are processed in-memory and never cached or stored on the hosting infrastructure.

Use it with your favorite AI tools

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

OpenAI Agents SDK sdk-python

Google ADK sdk-python

Pydantic AI sdk-python

Vercel AI SDK sdk-typescript