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csjoblom/musclesworked-mcp

by Various

MCP server for exercise-to-muscle mapping. 856 exercises, 65 muscles, 14 muscle groups with primary/secondary/stabilizer activation data.

C

MCP

csjoblom/musclesworked-mcp

Added 1 June 2026

#ai #claude #exercise #fitness #mcp #mcp-server #model-context-protocol #muscle

Overview

An MCP server that maps 856 exercises to 65 muscles across 14 muscle groups. Provides primary, secondary, and stabilizer activation data via the Model Context Protocol.

Best for

Best for
Developers building fitness or workout applications that need detailed exercise-to-muscle relationship data

Use cases

  • Query muscle activation data for specific exercises in a fitness app backend
  • Generate workout plans that target distinct muscle groups using structured exercise metadata
  • Analyze exercise variety and muscle coverage for training programs

How to use

Install

claude mcp add musclesworked -- npx -y musclesworked-mcp --api-key mw_live_...

Tools exposed

  • get_muscles_worked
  • find_exercises
  • analyze_workout
  • get_alternatives
  • search_exercises
  • search_muscles
  • MUSCLESWORKED_API_URL

Tested with

Claude Desktop, Claude Code, Cursor

Example client config

{\n  "mcpServers": {\n    "musclesworked": {\n      "command": "npx",\n      "args": ["-y", "musclesworked-mcp"],\n      "env": {\n        "MUSCLESWORKED_API_KEY": "mw_live_..."\n      }\n    }\n  }\n}

Notes

An MCP server that maps 856 exercises to 65 muscles across 14 muscle groups. Provides primary, secondary, and stabilizer activation data via the Model Context Protocol.

2 stars on GitHub. Last updated 2026-02-17. Licensed MIT.

Use cases

  • Query muscle activation data for specific exercises in a fitness app backend
  • Generate workout plans that target distinct muscle groups using structured exercise metadata
  • Analyze exercise variety and muscle coverage for training programs

Pros

  • Large exercise database with standardized muscle categorization
  • Includes stabilizer activation details, not just primary and secondary
  • Structured machine-readable format via MCP protocol

Cons

  • Very low community adoption (2 GitHub stars) suggests limited use and testing
  • Only provides muscle mapping, no exercise instructions, images, or equipment data
  • Requires an MCP-compatible client to consume the server endpoints

Indexed from awesome-mcp-servers-punkpeye and enriched against its public facts.

Pros

  • Large exercise database with standardized muscle categorization
  • Includes stabilizer activation details, not just primary and secondary
  • Structured machine-readable format via MCP protocol

Cons

  • Very low community adoption (2 GitHub stars) suggests limited use and testing
  • Only provides muscle mapping, no exercise instructions, images, or equipment data
  • Requires an MCP-compatible client to consume the server endpoints
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