How do I get FatSecret API access?

Register at platform.fatsecret.com for a free developer account. You'll receive a Client ID and Client Secret for OAuth 2.0 authentication.

Does FatSecret include data on restaurant chains and packaged foods?

Yes, the database includes a vast array of branded products, restaurant chains, and generic foods from around the world.

Are multiple serving sizes supported for foods?

Yes, most foods in the platform have multiple serving size options available, such as per 100g, per piece, or per cup, giving you highly accurate portion logging.

FatSecret MCP Server for LlamaIndex 2 tools — connect in under 2 minutes

Name: FatSecret
Availability: InStock
Author: Vinkius

docs.llamaindex.ai/en/stable/module_guides/tools/mcp

Built by Vinkius GDPR 2 Tools Framework

LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add FatSecret as an MCP tool provider through Vinkius and your agents can query, analyze, and act on live data alongside your existing indexes.

Get MCP Server for AI Agents

ASK AI ABOUT THIS MCP SERVER

Open in ChatGPT Open in Claude Open in Perplexity

Vinkius supports streamable HTTP and SSE.

python

import asyncio
from llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI

async def main():
    # Your Vinkius token. get it at cloud.vinkius.com
    mcp_client = BasicMCPClient("https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")
    mcp_tool_spec = McpToolSpec(client=mcp_client)
    tools = await mcp_tool_spec.to_tool_list_async()

    agent = FunctionAgent(
        tools=tools,
        llm=OpenAI(model="gpt-4o"),
        system_prompt=(
            "You are an assistant with access to FatSecret. "
            "You have 2 tools available."
        ),
    )

    response = await agent.run(
        "What tools are available in FatSecret?"
    )
    print(response)

asyncio.run(main())

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* Every MCP server runs on Vinkius-managed infrastructure inside AWS - a purpose-built runtime with per-request V8 isolates, Ed25519 signed audit chains, and sub-40ms cold starts optimized for native MCP execution. See our infrastructure

About FatSecret MCP Server

The FatSecret MCP Server connects your AI agent to one of the world's most popular food tracking platforms — trusted by 30 million+ users for diet management and calorie counting.

LlamaIndex agents combine FatSecret tool responses with indexed documents for comprehensive, grounded answers. Connect 2 tools through Vinkius and query live data alongside vector stores and SQL databases in a single turn. ideal for hybrid search, data enrichment, and analytical workflows.

Core Capabilities

Food Search — Find any food by name across a massive database of generic and branded products.
Detailed Nutrition — Full macro breakdown per serving: calories, protein, fat, and carbohydrates.
Multiple Serving Sizes — Every food includes multiple serving size options (per cup, per 100g, per piece, etc.).
Brand Coverage — Extensive branded product database including restaurant chains and packaged goods.

Free developer plan available. OAuth 2.0 client credentials authentication.

The FatSecret MCP Server exposes 2 tools through the Vinkius. Connect it to LlamaIndex in under two minutes — no API keys to rotate, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.

How to Connect FatSecret to LlamaIndex via MCP

Follow these steps to integrate the FatSecret MCP Server with LlamaIndex.

Install dependencies

Run pip install llama-index-tools-mcp llama-index-llms-openai

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token

Run the agent

Save to agent.py and run: python agent.py

Explore tools

The agent discovers 2 tools from FatSecret

Why Use LlamaIndex with the FatSecret MCP Server

LlamaIndex provides unique advantages when paired with FatSecret through the Model Context Protocol.

Data-first architecture: LlamaIndex agents combine FatSecret tool responses with indexed documents for comprehensive, grounded answers

Query pipeline framework lets you chain FatSecret tool calls with transformations, filters, and re-rankers in a typed pipeline

Multi-source reasoning: agents can query FatSecret, a vector store, and a SQL database in a single turn and synthesize results

Observability integrations show exactly what FatSecret tools were called, what data was returned, and how it influenced the final answer

FatSecret + LlamaIndex Use Cases

Practical scenarios where LlamaIndex combined with the FatSecret MCP Server delivers measurable value.

Hybrid search: combine FatSecret real-time data with embedded document indexes for answers that are both current and comprehensive

Data enrichment: query FatSecret to augment indexed data with live information before generating user-facing responses

Knowledge base agents: build agents that maintain and update knowledge bases by periodically querying FatSecret for fresh data

Analytical workflows: chain FatSecret queries with LlamaIndex's data connectors to build multi-source analytical reports

FatSecret MCP Tools for LlamaIndex (2)

These 2 tools become available when you connect FatSecret to LlamaIndex via MCP:

get_fatsecret_food_details

g. 1 cup, 100g, 1 oz). Get detailed nutritional information for a specific food item with all serving sizes

search_fatsecret_foods

Returns calorie, protein, fat, and carb data per serving. Popular with fitness and diet tracking apps worldwide. Search the FatSecret food database for foods with calorie and macro data

Example Prompts for FatSecret in LlamaIndex

Ready-to-use prompts you can give your LlamaIndex agent to start working with FatSecret immediately.

"How many calories in a Big Mac?"

"Search for the nutrition data of a medium apple."

"What are the macros for a serving of whey protein powder?"

Troubleshooting FatSecret MCP Server with LlamaIndex

Common issues when connecting FatSecret to LlamaIndex through the Vinkius, and how to resolve them.

BasicMCPClient not found

Install: pip install llama-index-tools-mcp

FatSecret + LlamaIndex FAQ

Common questions about integrating FatSecret MCP Server with LlamaIndex.

How does LlamaIndex connect to MCP servers?

Use the MCP client adapter to create a connection. LlamaIndex discovers all tools and wraps them as query engine tools compatible with any LlamaIndex agent.

Can I combine MCP tools with vector stores?

Yes. LlamaIndex agents can query FatSecret tools and vector store indexes in the same turn, combining real-time and embedded data for grounded responses.