MyFitnessPal MCP Server
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Alternatives to MyFitnessPal MCP Server
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Related Servers
- AlicenseAqualityCmaintenanceEnables AI assistants to interact with MyFitnessPal data including food diary, exercises, body measurements, nutrition goals, and water intake through natural language.2058MIT
- AlicenseNot gradedqualityDmaintenanceEnables AI assistants to read and write MyFitnessPal data, including food diary, exercises, body measurements, nutrition goals, and water intake.MIT
- FlicenseNot gradedqualityDmaintenanceEnables logging food into MyFitnessPal diary via natural language, supporting search, log, quick add, and diary retrieval.-
- AlicenseNot gradedqualityAmaintenanceConnect MyFitnessPal to Claude or any MCP client. Log meals, search food database with macros, track trends, and export nutrition history against your real MyFitnessPal diary.137 PyPI13MIT
- AlicenseNot gradedqualityBmaintenanceEnables Claude to access your MyFitnessPal diary—foods, portions, meal and daily nutrition totals, water, exercise, steps, and weight—through a custom connector on web, desktop, and mobile.14 npmMIT
- AlicenseNot gradedqualityCmaintenanceEnables nutrition tracking via AI, allowing users to read food logs with macros, goals, and profile, log meals by text or photo, and access diary, subscription, diabetes, and wearable/glucose data.MIT
TDQS
Scored across 7 tools
Most tools are clearly distinct: search_food and get_food_details relate to the food database, while get_diary and get_nutrition_summary relate to daily records. There is minor overlap between get_diary and get_nutrition_summary since both expose calorie/macro totals, but their descriptions clarify the different levels of detail.
Tool names follow a consistent snake_case verb_noun pattern: get_diary, get_goals, search_food, add_food, quick_add_calories. The naming style is uniform and predictable across all seven tools.
Seven tools is a well-scoped size for a nutrition diary server. Each tool covers a meaningful part of the workflow—viewing data, searching foods, and logging entries—without unnecessary redundancy.
The server covers core read and add workflows: viewing diary data, searching foods, getting details, and adding entries. However, it lacks update and delete operations for diary entries, which is a notable gap when correcting mistakes or modifying logged food.