trainingpeaks-mcp vs memory

Side-by-side comparison to help you pick between these two MCP servers.

	trainingpeaks-mcp by JamsusMaximus	memory by modelcontextprotocol
Stars	★ 65	★ 85,748
30d uses	—	—
Score	48	77
Official	—	✓
Categories	Developer ToolsProductivityhealth	Knowledge GraphAI / LLM ToolsProductivity
Language	Python	TypeScript
Last commit	this month	this month

trainingpeaks-mcp · Summary

TrainingPeaks MCP server enables querying workouts, fitness data, and PRs via natural language.

memory · Summary

An MCP server implementing persistent memory using a local knowledge graph for AI models to remember user information across chats.

trainingpeaks-mcp · Use cases

Build structured training workouts with intervals and automatically calculated IF/TSS
Compare FTP progression and fitness metrics (CTL/ATL/TSB) between different periods
Schedule workouts and manage training calendar with natural language commands
Log health metrics and track progress over time

memory · Use cases

Personalizing AI assistant interactions by remembering user preferences, history, and relationships
Building context-aware chat applications that maintain conversation history
Creating knowledge bases that persist across AI model sessions

trainingpeaks-mcp · Install

Option A: Auto-Setup with Claude Code

If you have [Claude Code](https://claude.ai/code), paste this prompt:

Set up the TrainingPeaks MCP server from https://github.com/JamsusMaximus/trainingpeaks-mcp - clone it, create a venv, install it, then walk me through getting my TrainingPeaks cookie from my browser and run tp-mcp auth. Finally, add it to my Claude Desktop config.

Option B: Manual Setup

Step 1: Install

git clone https://github.com/JamsusMaximus/trainingpeaks-mcp.git
cd trainingpeaks-mcp
python3 -m venv .venv
source .venv/bin/activate  # Windows: .venv\Scripts\activate
pip install -e .

Step 2: Authenticate

pip install tp-mcp[browser]  # One-time: install browser support
tp-mcp auth --from-browser chrome  # Or: firefox, safari, edge, auto

Step 3: Add to Claude Desktop

Run this to get your config snippet:

tp-mcp config

Edit ~/Library/Application Support/Claude/claude_desktop_config.json (macOS) or %APPDATA%\Claude\claude_desktop_config.json (Windows) and paste it inside mcpServers:

memory · Install

Installation

Claude Desktop

Add to your claude_desktop_config.json:

{
  "mcpServers": {
    "memory": {
      "command": "npx",
      "args": [
        "-y",
        "@modelcontextprotocol/server-memory"
      ]
    }
  }
}

VS Code

Use one-click installation buttons or manually configure in .vscode/mcp.json:

{
  "servers": {
    "memory": {
      "command": "npx",
      "args": [
        "-y",
        "@modelcontextprotocol/server-memory"
      ]
    }
  }
}

Docker

{
  "mcpServers": {
    "memory": {
      "command": "docker",
      "args": ["run", "-i", "-v", "claude-memory:/app/dist", "--rm", "mcp/memory"]
    }
  }
}

Comparison generated from public README + GitHub signals. Last updated automatically.