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Antrenör MCP — 실험 / 학습 프로젝트

이것은 프로덕션이 아닙니다. 메인 Antrenör 애플리케이션의 MCP(Model Context Protocol) 버전을 실험하기 위해 만든 사이드 프로젝트입니다. 목적: MCP 아키텍처를 배우고, 메인 프로젝트에 통합되었을 때 어떻게 보일지 보여주는 것입니다.

아키텍처

mcp_servers/data/demo.db  (SQLite — uydurma seed veri)
        ↑                    ↑
   MCP server'lar        Streamlit
   (Claude Desktop)      (localhost:8501)

핵심 아이디어: 하나의 데이터 소스, 두 개의 다른 클라이언트 — 하나는 대화형(Claude Desktop + MCP), 하나는 시각적(Streamlit).

Related MCP server: fitness-mcp-server

메인 프로젝트와의 차이점

Ana Proje (üretim)

Bu Deney

모델 접근

푸시 모델 — Worker가 프롬프트에 컨텍스트를 붙여넣음

풀 모델 — Claude가 MCP 서버에서 가져옴

클라이언트

iOS + Streamlit

Claude Desktop + Streamlit

배포

Cloudflare Workers + D1

로컬 Python

데이터 소스

실제 Garmin API

가상의 SQLite 시드

목적

사용자에게 가치 창출

아키텍처 학습/시연

MCP 서버들

  1. garmin — 활동 기록, 웰니스, 심박수 구역 분포, 주간 부하 (SQLite에서)

  2. pain — 통증 기록 + 새 통증 로깅 (SQLite에 INSERT)

  3. coach-rules — coach-principles.md + pain-rubric.md 프롬프트 템플릿으로

자세한 내용 및 API: mcp_servers/README.md

빠른 시작

# 1. Bağımlılıklar
python3.11 -m venv .venv
.venv/bin/pip install -r requirements.txt

# 2. DB seed
.venv/bin/python -m mcp_servers.data.seed

# 3. Claude Desktop config kur
cp mcp_servers/claude_desktop_config.example.json \
   "$HOME/Library/Application Support/Claude/claude_desktop_config.json"

# 4. Claude Desktop'ı restart (Cmd+Q sonra yeniden aç)

# 5. Streamlit dashboard (opsiyonel, görsel demo için — port 8502, ana projenin 8501 ile çakışmaz)
.venv/bin/streamlit run streamlit_demo/app.py --server.port 8502

데모 시나리오

발표에 사용할 3가지 시나리오 및 흐름: DEMO.md

폴더 구조

antrenör_mcp/
├── mcp_servers/
│   ├── garmin/server.py       Aktivite + wellness + zone MCP
│   ├── pain/server.py         Ağrı geçmişi + log_pain tool
│   ├── coach_rules/server.py  Prensipler + rubric prompt template
│   ├── data/seed.py           DB seed script
│   ├── data/demo.db           SQLite (git'e girmez)
│   └── claude_desktop_config.example.json
├── streamlit_demo/app.py      3 tab dashboard
├── docs/
│   ├── coach-principles.md    Zone 2, shin, deload kuralları
│   └── pain-rubric.md         0-10 ağrı ölçeği
├── DEMO.md                    Sunum senaryosu
└── requirements.txt

Related MCP Connectors

  • Talk to your own gym log. Reps is a free workout tracker for iPhone and Android; connect it to Claude, ChatGPT or any MCP client and ask about your workout history, personal records, exercise progression, weekly summaries, routines and training plan. The assistant can also save a new routine, edit one, save a whole plan, add custom exercises and exercise notes, always after you confirm in the chat. It cannot log a workout or delete your history. Requires a free Reps account created in the app; you sign in with a one-time email code.

  • Pace is a remote MCP server that exposes wearable and fitness data to Claude via the Model Context Protocol. It connects to Garmin, Oura, Whoop, Polar, Fitbit and 20+ devices and provides 15 tools for querying sleep, activity, recovery, and training data. Hosted on Google Cloud Run, OAuth 2.1 authentication, Streamable HTTP transport. Instructions: First you need to create an account at: https://pacetraining.co and connect your wearables. After that you can connect the remote Server via Custom Connector in Claude and OAuth 2.1 Flow startet.

  • Apple Health training load, recovery, HRV and workout detail for Claude, ChatGPT and any MCP client.

  • Adaptive running coach MCP server — training data, plans, and recovery for AI assistants.

Related MCP Servers

  • A
    license
    A
    quality
    B
    maintenance
    Enables Claude to access and query your Garmin Connect data, including sleep, activities, training load, and health metrics, through a set of read-only MCP tools.
    28
    160 PyPI
    2
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  • F
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    quality
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    Provides MCP servers for Claude to access fitness data from Strava and intervals.icu, enabling natural language queries for activity analysis, advanced training metrics, and wellness tracking.
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  • F
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    quality
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    Enables workout tracking and coaching within Claude conversations, managing exercise configs, logs, streaks, and health metrics via an MCP server with PostgreSQL.
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