MCP YouTube Transcript
An MCP server retrieving transcripts of YouTube videos
Descripción del creador (original en inglés)
Self-hosted Intervals.icu MCP server: training analytics, Garmin data, wellness, workout planning
Estos tres nunca se combinan en una sola puntuación: las opiniones, las mediciones y la popularidad son cosas distintas.
Todavía no escribimos un resumen sencillo para este. Abajo están los datos que leímos del registro.
Elige la app de IA que usas y copia la configuración.
API_KEY, ATHLETE_ID, MCP_PERMISSIONS, MCP_TOOLSET, ATHLETE_TIMEZONELos valores marcados como secretos son llaves de tu cuenta. Nunca los compartas ni los pegues en un chat.
Necesitas
¿Qué app usas?
Elige tu app arriba. La recordamos, así que la próxima vez el botón dirá “Instalar en …”.
Abre Claude Desktop → Settings → Developer → Edit Config y pega esto en 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"
}
}
}
}Ejecuta esto en tu 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-mcpCompleta los valores vacíos antes de ejecutarlo.
Pégalo en .cursor/mcp.json de tu proyecto, o en ~/.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"
}
}
}
}Tu navegador te pide permiso para abrir la app; luego la app muestra la configuración y te pide confirmar. Los valores de las claves quedan vacíos para que los completes tú.
Pégalo en .vscode/mcp.json de tu proyecto
{
"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"
}
}
}
}Tu navegador te pide permiso para abrir la app; luego la app muestra la configuración y te pide confirmar. Los valores de las claves quedan vacíos para que los completes tú.
Este se ejecuta en tu computadora, y ChatGPT solo puede conectarse a servidores en internet. Elige otra app.
Tu elección se guarda en una cookie en este dispositivo; no necesitas cuenta.
MCP_PERMISSIONS envMCP_TOOLSET envATHLETE_TIMEZONE envEsta configuración se genera a partir de la ficha del creador en el registro. Si el README dice otra cosa, sigue el README.
Se ejecuta en tu computadora; la lista de herramientas aparece en tu app de IA después de instalarlo.
Revisamos información que cualquiera puede ver; no hemos leído el código. Las revisiones que no aplican no se cuentan. · Revisado el 6 nov 2026
El nombre en el registro se verificó con la cuenta de GitHub futureweb, dueña del repo
Todavía no hemos leído este repo en GitHub
Todavía no hemos leído este repo en GitHub
Este se ejecuta en tu computadora; no hay endpoint en internet
Se ejecuta en tu computadora y usa ajustes locales en su lugar
Se ejecuta en tu computadora; solo podemos leer listas de herramientas de endpoints en internet
Comparado con todos los servidores activos de esta categoría en el registro. Las estrellas solo cuentan cuando quien lo publica es dueño del repo.
Superó 1 de 1 revisiones básicas aplicables. No hemos leído el código. Antes de agregar una clave o un token, revisa qué acceso pide el servidor y usa una clave que tenga solo los permisos que necesitas.
Debes completar: API_KEY, ATHLETE_ID
Se ejecuta en tu computadora. Tu app de IA lo inicia cuando hace falta, y los datos no pasan por los servidores del creador (a menos que el propio servidor llame a un servicio en línea).
El repo de GitHub cambió por última vez –, y la última versión (1.0.0b2) llegó al registro hace 22 días.
Las apps compatibles con MCP, como Claude Desktop, Claude Code, Cursor y VS Code. También necesitas Python (uv), Docker en tu computadora.
En la misma categoría: MCP YouTube Transcript, Google Analytics MCP, BibiGPT YouTube Video Summarizer
Servidores de la misma categoría hechos por otros creadores, ordenados por estrellas.
Cómo leer una tarjeta:★ Estrellas de GitHubiTres pilares: reseñas · revisiones · usoiActualizado = fecha en que se publicó la última versión en el registroi
An MCP server retrieving transcripts of YouTube videos
GA4 MCP server for AI agents: schema discovery, server-side aggregation, analysis-ready defaults.
AI video summarizer: summarize and transcribe YouTube, Bilibili, TikTok and podcast videos.
Copiado tal cual del repo del creador. PopNx MCP no lo ha revisado ni lo respalda. Se quitaron HTML, imágenes y scripts. Los enlaces llevan fuera de este sitio.
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.
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.private_key_jwt (ChatGPT), PKCE and RFC 9207. Streamable HTTP (/mcp) and SSE from one process.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.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, write, destructive, admin) enforced on the server; only read is active unless you enable more.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.
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
Solo se muestra el principio; lee el resto en el repo