Fitbit MCP Server for LlamaIndex 14 tools — connect in under 2 minutes
LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add Fitbit as an MCP tool provider through Vinkius and your agents can query, analyze, and act on live data alongside your existing indexes.
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Vinkius supports streamable HTTP and SSE.
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 Fitbit. "
"You have 14 tools available."
),
)
response = await agent.run(
"What tools are available in Fitbit?"
)
print(response)
asyncio.run(main())
* 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 Fitbit MCP Server
Connect your Fitbit account to any AI agent and gain instant access to your comprehensive health and fitness data through natural conversation.
LlamaIndex agents combine Fitbit tool responses with indexed documents for comprehensive, grounded answers. Connect 14 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.
What you can do
- Activity Tracking — Retrieve daily activity summaries including steps, distance, calories, and active minutes for any date
- Sleep Analysis — Access detailed sleep logs with stages (deep, light, REM, awake) for individual nights or time series trends
- Heart Rate Monitoring — Query resting heart rate, intraday zones, and historical cardiac trends
- SpO2 & Breathing — View blood oxygen saturation levels and breathing rate data
- Body Composition — Track weight measurements and cardio fitness scores over time
- Nutrition Logs — Access water intake and food logging data for dietary tracking
- Device Management — Check connected Fitbit devices and their sync status
The Fitbit MCP Server exposes 14 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 Fitbit to LlamaIndex via MCP
Follow these steps to integrate the Fitbit 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 14 tools from Fitbit
Why Use LlamaIndex with the Fitbit MCP Server
LlamaIndex provides unique advantages when paired with Fitbit through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine Fitbit tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain Fitbit tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query Fitbit, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what Fitbit tools were called, what data was returned, and how it influenced the final answer
Fitbit + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the Fitbit MCP Server delivers measurable value.
Hybrid search: combine Fitbit real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query Fitbit 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 Fitbit for fresh data
Analytical workflows: chain Fitbit queries with LlamaIndex's data connectors to build multi-source analytical reports
Fitbit MCP Tools for LlamaIndex (14)
These 14 tools become available when you connect Fitbit to LlamaIndex via MCP:
get_activities_date
Returns steps, calories burned, distance walked, active minutes, floors climbed, elevation and sedentary minutes. Date format: YYYY-MM-DD or "today". Get activity summary for a specific date
get_activities_timeseries
Resource paths: "steps", "calories", "distance", "floors", "elevation", "minutesSedentary", "minutesLightlyActive", "minutesFairlyActive", "minutesVeryActive", "activityCalories". Period: 1d, 7d, 30d, 1w, 1m, 3m, 6m, 1y, max or startDate/endDate (YYYY-MM-DD). Detail level: "1min", "5min", "15min", "1day" for intraday data. Get activity time series data over a date range
get_body_weight
Returns weight in kg, BMI, fat percentage and date logged. Date format: YYYY-MM-DD. Get body weight log entries for a specific date
get_breathing_rate
Returns breathing rate in breaths per minute. Available on Fitbit devices with SpO2 sensors. Date format: YYYY-MM-DD. Get breathing rate for a specific date
get_cardio_fitness_score
Returns VO2 Max values and percentile rankings. Date format: YYYY-MM-DD. Get cardio fitness score (VO2 Max) for a date range
get_devices
Returns device version, MAC address, battery level, last sync time and device type. Get all Fitbit devices connected to the user's account
get_foods_date
Returns total calories consumed, macros (carbs, protein, fat), water intake and list of logged foods with meal times. Date format: YYYY-MM-DD or "today". Get food log summary for a specific date
get_heart_date
Returns resting heart rate, heart rate zones (fat burn, cardio, peak, out of range) and calories burned in each zone. Date format: YYYY-MM-DD or "today". Get heart rate summary for a specific date
get_heart_timeseries
Returns resting heart rate and heart rate zones per day. Detail level: "1min", "5min", "15min", "1day" for intraday BPM data. Get heart rate time series data over a date range
get_profile
Returns display name, full name, age, height, weight, gender, locale, timezone, avatar URL and member since date. Get the authenticated user's Fitbit profile
get_sleep_date
Returns sleep start time, duration, minutes asleep, minutes awake, minutes in each sleep stage (light, deep, REM, awake), efficiency score and number of awakenings. Date format: YYYY-MM-DD or "today". Get sleep log for a specific date
get_sleep_timeseries
Returns daily sleep summaries with start time, duration, minutes asleep, efficiency and sleep stages. Date range format: startDate/endDate (YYYY-MM-DD). Get sleep log over a date range
get_spo2
Returns average SpO2 percentage and min/max values. Available on Fitbit devices with SpO2 sensors. Date format: YYYY-MM-DD. Get blood oxygen saturation (SpO2) for a specific date
get_water
Returns water consumption in milliliters and timestamps. Date format: YYYY-MM-DD. Get water intake log for a specific date
Example Prompts for Fitbit in LlamaIndex
Ready-to-use prompts you can give your LlamaIndex agent to start working with Fitbit immediately.
"How did I sleep last night?"
"Show my heart rate trends for the past week."
Troubleshooting Fitbit MCP Server with LlamaIndex
Common issues when connecting Fitbit to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpFitbit + LlamaIndex FAQ
Common questions about integrating Fitbit MCP Server with LlamaIndex.
How does LlamaIndex connect to MCP servers?
Can I combine MCP tools with vector stores?
Does LlamaIndex support async MCP calls?
Connect Fitbit with your favorite client
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Anthropic's agentic CLI for terminal-first development.
Python SDK for building production-grade OpenAI agent workflows.
Google's framework for building production AI agents.
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TypeScript toolkit for building AI-powered web applications.
TypeScript-native agent framework for modern web stacks.
Python framework for orchestrating collaborative AI agent crews.
Leading Python framework for composable LLM applications.
Data-aware AI agent framework for structured and unstructured sources.
Microsoft's framework for multi-agent collaborative conversations.
Connect Fitbit to LlamaIndex
Get your token, paste the configuration, and start using 14 tools in under 2 minutes. No API key management needed.