Can I query sleep data for a specific date range?

Yes! Use the sleep time series tool to query sleep trends across any date range. You can also inspect a single night in detail with the sleep date tool, including all sleep stages.

What health metrics can I access?

You can access 14 different health metrics: activities, sleep (date & time series), heart rate (date & time series), SpO2, breathing rate, cardio fitness score, body weight, water intake, food logs, device info, and user profile.

Fitbit MCP Server for Pydantic AI 14 tools — connect in under 2 minutes

Name: Fitbit
Availability: InStock
Author: Vinkius

ai.pydantic.dev/mcp

Built by Vinkius GDPR 14 Tools SDK

Pydantic AI brings type-safe agent development to Python with first-class MCP support. Connect Fitbit through Vinkius and every tool is automatically validated against Pydantic schemas. catch errors at build time, not in production.

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 pydantic_ai import Agent
from pydantic_ai.mcp import MCPServerHTTP

async def main():
    # Your Vinkius token. get it at cloud.vinkius.com
    server = MCPServerHTTP(url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")

    agent = Agent(
        model="openai:gpt-4o",
        mcp_servers=[server],
        system_prompt=(
            "You are an assistant with access to Fitbit "
            "(14 tools)."
        ),
    )

    result = await agent.run(
        "What tools are available in Fitbit?"
    )
    print(result.data)

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 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.

Pydantic AI validates every Fitbit tool response against typed schemas, catching data inconsistencies at build time. Connect 14 tools through Vinkius and switch between OpenAI, Anthropic, or Gemini without changing your integration code. full type safety, structured output guarantees, and dependency injection for testable agents.

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 Pydantic AI 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 Pydantic AI via MCP

Follow these steps to integrate the Fitbit MCP Server with Pydantic AI.

Install Pydantic AI

Run pip install pydantic-ai

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 with type-safe schemas

Why Use Pydantic AI with the Fitbit MCP Server

Pydantic AI provides unique advantages when paired with Fitbit through the Model Context Protocol.

Full type safety: every MCP tool response is validated against Pydantic models, catching data inconsistencies before they reach your application

Model-agnostic architecture. switch between OpenAI, Anthropic, or Gemini without changing your Fitbit integration code

Structured output guarantee: Pydantic AI ensures tool results conform to defined schemas, eliminating runtime type errors

Dependency injection system cleanly separates your Fitbit connection logic from agent behavior for testable, maintainable code

Fitbit + Pydantic AI Use Cases

Practical scenarios where Pydantic AI combined with the Fitbit MCP Server delivers measurable value.

Type-safe data pipelines: query Fitbit with guaranteed response schemas, feeding validated data into downstream processing

API orchestration: chain multiple Fitbit tool calls with Pydantic validation at each step to ensure data integrity end-to-end

Production monitoring: build validated alert agents that query Fitbit and output structured, schema-compliant notifications

Testing and QA: use Pydantic AI's dependency injection to mock Fitbit responses and write comprehensive agent tests

Fitbit MCP Tools for Pydantic AI (14)

These 14 tools become available when you connect Fitbit to Pydantic AI 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 Pydantic AI

Ready-to-use prompts you can give your Pydantic AI 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 Pydantic AI

Common issues when connecting Fitbit to Pydantic AI through the Vinkius, and how to resolve them.

MCPServerHTTP not found

Update: pip install --upgrade pydantic-ai

Fitbit + Pydantic AI FAQ

Common questions about integrating Fitbit MCP Server with Pydantic AI.

How does Pydantic AI discover MCP tools?

Create an MCPServerHTTP instance with the server URL. Pydantic AI connects, discovers all tools, and generates typed Python interfaces automatically.

Does Pydantic AI validate MCP tool responses?

Yes. When you define result types as Pydantic models, every tool response is validated against the schema. Invalid data raises a clear error instead of silently corrupting your pipeline.

Can I switch LLM providers without changing MCP code?

Absolutely. Pydantic AI abstracts the model layer. your Fitbit MCP integration works identically with OpenAI, Anthropic, Google, or any supported provider.