Futureweb Intervals MCP

Name: Futureweb Intervals MCP
Author: futureweb

by futurewebversion 1.0.0b2i★ –iRuns on your computer

Registry nameio.github.futureweb/intervals-mcp-server

Install

Overview

Description from the maker (original English)

Self-hosted Intervals.icu MCP server: training analytics, Garmin data, wellness, workout planning

PopNx MCP’s three pillars

These three are never merged into one score: opinions, measurements and popularity are different things.

User voiceiNo reviews yetReviews open together with accounts in a later step
Our basic checksi1 of 1 passedChecked from public information; code not scanned yet
UsageiNot measured yet★ –

What you can do with it

We have not written a plain-language summary for this one yet. Below are the facts we read from the registry.

Connection: Runs on your computer
Transport: stdio
Category: Marketing & social media
You need to fill in: API_KEY, ATHLETE_ID

Install

Pick the AI app you use, then copy the settings.

Before you install

Basic checks: 1 of 1 passed
Runs on your computer with your user’s permissions (files, network, apps).
You fill in yourself: API_KEY, ATHLETE_ID, MCP_PERMISSIONS, MCP_TOOLSET, ATHLETE_TIMEZONE

Values marked secret are keys to your account. Never share them or paste them into a chat.

You need

PyPIPython’s uv installed (for the uvx command)

Which app do you use?

ClaudeClaude CodeCursorVS CodeChatGPT

Pick your app above. We remember it, so next time the button says “Install in …”.

Open Claude Desktop → Settings → Developer → Edit Config, and paste this into claude_desktop_config.json

{
  "mcpServers": {
    "intervals": {
      "command": "uvx",
      "args": [
        "futureweb-intervals-mcp"
      ],
      "env": {
        "API_KEY": "your-value",
        "ATHLETE_ID": "your-value",
        "MCP_PERMISSIONS": "your-value",
        "MCP_TOOLSET": "your-value",
        "ATHLETE_TIMEZONE": "your-value"
      }
    }
  }
}

Run this in your terminal

claude mcp add intervals --env API_KEY=your-value --env ATHLETE_ID=your-value --env MCP_PERMISSIONS=your-value --env MCP_TOOLSET=your-value --env ATHLETE_TIMEZONE=your-value -- uvx futureweb-intervals-mcp

Fill in the empty values before running it.

Open in Cursor

Or copy the settings yourself

Paste into .cursor/mcp.json in your project, or ~/.cursor/mcp.json

{
  "mcpServers": {
    "intervals": {
      "command": "uvx",
      "args": [
        "futureweb-intervals-mcp"
      ],
      "env": {
        "API_KEY": "your-value",
        "ATHLETE_ID": "your-value",
        "MCP_PERMISSIONS": "your-value",
        "MCP_TOOLSET": "your-value",
        "ATHLETE_TIMEZONE": "your-value"
      }
    }
  }
}

Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.

Open in VS Code

Or copy the settings yourself

Paste into .vscode/mcp.json in your project

{
  "servers": {
    "intervals": {
      "type": "stdio",
      "command": "uvx",
      "args": [
        "futureweb-intervals-mcp"
      ],
      "env": {
        "API_KEY": "your-value",
        "ATHLETE_ID": "your-value",
        "MCP_PERMISSIONS": "your-value",
        "MCP_TOOLSET": "your-value",
        "ATHLETE_TIMEZONE": "your-value"
      }
    }
  }
}

Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.

This one runs on your computer, and ChatGPT can only connect to servers on the internet. Pick another app.

Your choice stays on this device in a cookie; no account needed.

Values you fill in

API_KEY secret required env
Intervals.icu API key (Settings > Developer Settings)
ATHLETE_ID required env
Intervals.icu athlete ID
MCP_PERMISSIONS env
Enabled tool classes: read (default, read-only), read,write, or all (adds destructive and admin tools)
MCP_TOOLSET env
full (every tool of the enabled classes) or core (curated coaching set)
ATHLETE_TIMEZONE env
Time zone for 'today' and default date ranges, e.g. Europe/Vienna; default: the Intervals.icu athlete profile

These settings are generated from the maker’s registry entry. If the README says otherwise, follow the README.

What it can do

Runs on your computer; the tool list appears in your AI app after you install it.

Basic checksi

We check information anyone can see; we have not read the code. Checks that do not apply are left out of the count. · Checked Nov 6, 2026

✓ Pass
Maker matches the repo
The registry name was verified with the GitHub account futureweb, which owns the repo
… Not checked yet
Still maintained
We have not read this repo from GitHub yet
… Not checked yet
Has a licence
We have not read this repo from GitHub yet
– Not applicable
Endpoint responds
This one runs on your computer; there is no internet endpoint
– Not applicable
Sign-in with OAuth
Runs on your computer and uses local settings instead
… Not checked yet
No hidden instructions in tool descriptions
Runs on your computer; we can only read tool lists from internet endpoints

Compared with others in Marketing & social mediai

More stars than 0% of the other 2961 servers in this category
Published to the registry more recently than 58% of the other 2961 servers in this category
Basic checks equal to or better than 100% of the other 2961 servers in this category

Compared with every active server in this category in the registry. Stars count only when the publisher owns the repo.

Common questions

Is Futureweb Intervals MCP safe?

It passed 1 of 1 applicable basic checks. We have not read the code. Before adding a key or token, see what access the server asks for and use a key with only the permissions you need.

What keys or codes does it need?

You need to fill in: API_KEY, ATHLETE_ID

Does it run on my computer or in the cloud?

It runs on your computer. Your AI app starts it when needed, and data does not pass through the maker’s servers (unless it calls an online service itself).

Is it still maintained?

The GitHub repo last changed –, and the latest version (1.0.0b2) reached the registry 22 days ago.

Which apps can use it?

Apps that support MCP, such as Claude Desktop, Claude Code, Cursor and VS Code. You also need Python (uv), Docker on your computer.

What can I use instead?

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README from the maker

Open the README

Copied from the maker’s repo as written. PopNx MCP has not reviewed or endorsed it. HTML, images and scripts are removed. Links lead off this site.

Futureweb Intervals MCP

Advanced Intervals.icu MCP server for ChatGPT, Claude and every MCP client: Garmin-enriched metrics, every custom field and stream, recovery insights and endurance performance analysis.

A Model Context Protocol server that lets AI assistants read and analyse your Intervals.icu training data the way a coach would: activities with every custom field and stream, intervals, wellness against personal baselines, thresholds and zones, planned-versus-executed workouts, climbs, dual power meters, best efforts, efficiency and fatigue resistance, nutrition and weight, training summaries and the calendar. It runs where you run it, talks only to the Intervals.icu API and exposes nothing that writes unless you enable it.

Continuation of [mvilanova/intervals-mcp-server](https://github.com/mvilanova/intervals-mcp-server).

The original project is no longer actively developed (last commit on 2 August 2026, 29

pull requests left open). This community-maintained fork carries it on: the useful

open pull requests were reviewed and merged with their authors credited, the bugs fixed, and the

server rebuilt around coaching analysis, Garmin data and safe remote access. Not affiliated with

Intervals.icu or Garmin.

Companion project: the Garmin Intervals Bridge puts back the metrics Garmin strips from the files it sends to Intervals.icu (stamina, training effect, recovery time, VO₂max, running dynamics, sleep and HRV details …). This MCP is built to read all of it, but works just as well without the bridge.

For the mountains: intervals-summits writes the summits, huts and passes of your activities into custom fields (Summits, SummitCount). This MCP reads them like any other field, so your assistant knows which summits a tour reached: ask which ones you climbed this year, or how often.

Highlights

Every custom item, resolved dynamically. Custom activity fields, interval fields, streams and wellness fields are read from your own definitions and reported with name, code, value and units. Nothing device-specific is hard-coded; null, NaN, zero and absent stay distinct, fields that do not belong to the sport are kept apart, and device loads are never mixed with the Intervals.icu load.
Streams at full resolution. Any stream as summary, CSV or JSON, sliced by index or time, downsampled and paged, plus per-interval statistics of any stream (for example the stamina drop of each interval).
Coaching analysis instead of raw dumps. One-call activity report, planned versus executed per step (also for deleted events and rides extended beyond the plan), climbs and descents, second power meter check, best efforts, similar intervals, repeated workouts over time, power-to-heart-rate efficiency, fatigue resistance, training load (acute:chronic ratio, monotony, strain), three-zone intensity distribution with polarization index, aerobic durability, load projection over the plan, recovery snapshot with 42-day baselines, wellness trends and correlations, nutrition and weight trends, weekly and monthly summaries. Statistics with their sample sizes; the interpretation stays with the coach.
Token-efficient. detail_level (compact, standard, full) and output_format="json" on the heavy tools, a compact tool catalogue (about 23k tokens for read and write, an optional core set of about 10k), 13 ready-made coaching prompts and method guides as MCP resources and through get_guide.
Safe remote access. Built-in OAuth 2.1 server with "Continue with Intervals.icu" sign-in, a consent page with per-connection permissions, client metadata documents with private_key_jwt (ChatGPT), PKCE and RFC 9207. Streamable HTTP (/mcp) and SSE from one process.
Safe, verifiable writes. Workouts are validated before they are written, dry_run shows the exact request, a second identical event on the same day is refused, and every write reads back what Intervals.icu stored and parsed.
Route-aware outdoor workouts. Steps that end on the lap button (until_lap_press) let you start the next interval where the road suits it, e.g. at the foot of a climb, while the work steps stay timed. Confirmed on a Garmin Edge 1040 on the bike (see below).
Read-only by default. Tools are grouped into permission classes (read, write, destructive, admin) enforced on the server; only read is active unless you enable more.
Tested. More than 1,100 tests on synthetic data, ruff, mypy, CodeQL, pinned GitHub Actions, build and Docker smoke tests on every pull request.

Works with the Garmin Intervals Bridge

Garmin device ──► Garmin Connect ──► official sync ──► Intervals.icu activity (filtered FIT)

│ ▲ └── Garmin Intervals Bridge ──────────┘ restores custom fields, streams, wellness │ Futureweb Intervals MCP (this project) │ ChatGPT / Claude / any MCP client

The Garmin Intervals Bridge writes the metrics Garmin filters out into the custom activity fields, custom streams and wellness fields you defined in Intervals.icu. The MCP reads those definitions at run time, so every restored value shows up in the tools automatically:

Restored by the bridge	Where the MCP shows it
Training effect, EPOC, Garmin training load, recovery time, VO₂max, performance condition, stamina start/end, sweat loss, temperatures	`get_activity_details`, `get_activity_report`, `get_training_summary`, `get_training_load` (device loads apart from the Intervals.icu load), `get_fueling_analysis` (sweat loss per hour), `get_activity_data_audit` (which fields still hold a zero placeholder or no value)
Stamina and potential stamina, second power meter, grade-adjusted speed, gear selection, running dynamics streams	`get_activity_streams`, `get_activity_intervals`, `analyze_workout_execution`, `analyze_climbs`, `compare_power_streams`
Night SpO₂, respiration, sleeping HR, HRV details, Body Battery, sleep stages and stress, readiness, nutrition	`get_wellness_data`, `get_recovery_snapshot`, `get_wellness_trends`, `get_nutrition_summary`

The MCP never contacts Garmin; the bridge is optional and other devices or sync tools that fill custom items get the same treatment. Worked examples and notes on reading the device metrics correctly: docs/GARMIN_BRIDGE.md.

Tools

67 tools; 52 of them only read. Write tools are marked ✎ (write), ✖ (destructive) or ⚙ (admin) and are hidden unless their class is enabled. Most tools accept output_format="json" and detail_level (compact first). Tools never take an API key: credentials come only from the server environment.

The catalogue is kept small for the AI client: short descriptions that say when to use a tool, a description and (where the values are fixed) an enum for every parameter, and text results without a duplicate structured copy. Method details and the workout format live in MCP resources (intervals://methods/ , intervals://workout-syntax) and in get_guide(topic) for clients that only call tools. tools/list is about 22.7k tokens at MCP_PERMISSIONS=read,write (was 42.0k; cl100k); see Tool sets for a smaller set.

Activity analysis

Tool What it does

get_activity_report Complete analysis in one call: overview with fueling (carbs used/ingested per hour, sweat loss, energy), weather (temperature, feels-like, wind, head/tailwind share) and W′ balance (max depletion, time below 75/50/25 % of W′), plan vs execution or intervals, second power meter check, climbs, data-quality notes (source, upload and analysis times, recording stops, zero placeholders); include_route_history compares earlier activities on the same Intervals.icu route (time, power, W/kg, HR, weather, stamina); detail_level compact (core numbers, one context line and key findings, about 2-2.5k characters), standard or full

get_activity_data_audit Provenance and data quality of one activity: source and file (Garmin sync, Garmin export or bridge upload, Strava stub), upload and analysis times, filtered duplicates, recording stops and gaps, FIT laps vs intervals and manual edits, device and sensor identity, streams usual for the sport but missing, per-stream coverage, custom fields with value / zero placeholder / none and what the Garmin Intervals Bridge could fill; without an activity, the per-sport data coverage of a period

| analyze_workout_execution | Planned vs executed per step (duration, target adherence, time in range, HR response, fade, Pw:HR drift with the Intervals.icu decoupling sign, stamina). A step ma

Only the beginning is shown; read the rest in the repo

Futureweb Intervals MCP

Overview

PopNx MCP’s three pillars

What you can do with it

Install

Before you install

Values you fill in

What it can do

Basic checksi

Maker matches the repo

Still maintained

Has a licence

Endpoint responds

Sign-in with OAuth

No hidden instructions in tool descriptions

Compared with others in Marketing & social mediai

Common questions

Alternatives

MCP YouTube Transcript

Google Analytics MCP

BibiGPT YouTube Video Summarizer

README from the maker

Futureweb Intervals MCP

Contents

Highlights

Works with the Garmin Intervals Bridge

Tools