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MyFitnessPal MCP

by Mason-Levyyversion 0.5.0i★ –iRuns on your computer
Registry nameio.github.Mason-Levyy/mfp-mcp

Install

Overview

Description from the maker (original English)

Log MyFitnessPal by talking to your AI: search foods with macros, log meals, weight, water and more.

PopNx MCP’s three pillars

These three are never merged into one score: opinions, measurements and popularity are different things.

What you can do with it

We have not written a plain-language summary for this one yet. Below are the facts we read from the registry.

  • Connection: Runs on your computer
  • Transport: stdio
  • Category: Search & AI memory
  • You need to fill in: MFP_COOKIE

Install

Pick the AI app you use, then copy the settings.

Before you install

  • Basic checks: 1 of 1 passed
  • Runs on your computer with your user’s permissions (files, network, apps).
  • You fill in yourself: MFP_COOKIE, MFP_USERNAME

Values marked secret are keys to your account. Never share them or paste them into a chat.

You need

  • PyPIPython’s uv installed (for the uvx command)

Which app do you use?

Pick your app above. We remember it, so next time the button says “Install in …”.

Open Claude Desktop → Settings → Developer → Edit Config, and paste this into claude_desktop_config.json

{
  "mcpServers": {
    "mfp": {
      "command": "uvx",
      "args": [
        "mfp-mcp"
      ],
      "env": {
        "MFP_COOKIE": "your-value",
        "MFP_USERNAME": "your-value"
      }
    }
  }
}

Run this in your terminal

claude mcp add mfp --env MFP_COOKIE=your-value --env MFP_USERNAME=your-value -- uvx mfp-mcp

Fill in the empty values before running it.

Open in Cursor

Or copy the settings yourself

Paste into .cursor/mcp.json in your project, or ~/.cursor/mcp.json

{
  "mcpServers": {
    "mfp": {
      "command": "uvx",
      "args": [
        "mfp-mcp"
      ],
      "env": {
        "MFP_COOKIE": "your-value",
        "MFP_USERNAME": "your-value"
      }
    }
  }
}

Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.

Open in VS Code

Or copy the settings yourself

Paste into .vscode/mcp.json in your project

{
  "servers": {
    "mfp": {
      "type": "stdio",
      "command": "uvx",
      "args": [
        "mfp-mcp"
      ],
      "env": {
        "MFP_COOKIE": "your-value",
        "MFP_USERNAME": "your-value"
      }
    }
  }
}

Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.

This one runs on your computer, and ChatGPT can only connect to servers on the internet. Pick another app.

Your choice stays on this device in a cookie; no account needed.

Values you fill in

  • MFP_COOKIE secret env
    MyFitnessPal session cookie (optional; 'mfp-mcp auth' is the guided alternative)
  • MFP_USERNAME env
    MyFitnessPal username; only needed if MFP's profile lookup fails for your account

These settings are generated from the maker’s registry entry. If the README says otherwise, follow the README.

What it can do

Runs on your computer; the tool list appears in your AI app after you install it.

Basic checksi

We check information anyone can see; we have not read the code. Checks that do not apply are left out of the count. · Checked Nov 8, 2026

  • ✓ Pass

    Maker matches the repo

    The registry name was verified with the GitHub account Mason-Levyy, which owns the repo

  • … Not checked yet

    Still maintained

    We have not read this repo from GitHub yet

  • … Not checked yet

    Has a licence

    We have not read this repo from GitHub yet

  • – Not applicable

    Endpoint responds

    This one runs on your computer; there is no internet endpoint

  • – Not applicable

    Sign-in with OAuth

    Runs on your computer and uses local settings instead

  • … Not checked yet

    No hidden instructions in tool descriptions

    Runs on your computer; we can only read tool lists from internet endpoints

Compared with others in Search & AI memoryi

  • More stars than 0% of the other 5670 servers in this category
  • Published to the registry more recently than 90% of the other 5670 servers in this category
  • Basic checks equal to or better than 100% of the other 5670 servers in this category

Compared with every active server in this category in the registry. Stars count only when the publisher owns the repo.

Common questions

Is MyFitnessPal MCP safe?

It passed 1 of 1 applicable basic checks. We have not read the code. Before adding a key or token, see what access the server asks for and use a key with only the permissions you need.

What keys or codes does it need?

You need to fill in: MFP_COOKIE

Does it run on my computer or in the cloud?

It runs on your computer. Your AI app starts it when needed, and data does not pass through the maker’s servers (unless it calls an online service itself).

Is it still maintained?

The GitHub repo last changed –, and the latest version (0.5.0) reached the registry 4 days ago.

Which apps can use it?

Apps that support MCP, such as Claude Desktop, Claude Code, Cursor and VS Code. You also need Python (uv) on your computer.

What can I use instead?

In the same category: Context7, Codebase Memory, Exa

Alternatives

Servers in the same category from other makers, sorted by stars.

Reading a card:★ GitHub starsiThree pillars: reviews · checks · usageiUpdated = latest version published to the registryi

Context7

by upstash

Feeds AI the latest docs for libraries and frameworks before it answers, so code it writes does not use outdated, removed functions.

Computer or cloud★ 64,224MITUpdated yesterday
Install

Codebase Memory

by DeusData

Codebase knowledge graph for AI agents — 162 languages, sub-ms queries, 99% fewer tokens.

Runs on your computer★ 48,148MITUpdated 21 days ago
Install

Exa

by exa-labs

Connect AI agents to Exa for web search, content fetching, and multi-step research.

Runs in the cloud★ 5,213MITUpdated 2 months ago
Install

README from the maker

Open the README

Copied from the maker’s repo as written. PopNx MCP has not reviewed or endorsed it. HTML, images and scripts are removed. Links lead off this site.

Log MyFitnessPal by talking to your AI.

Quickstart · Tools · Authentication · Changelog

▶ Watch the launch video

MyFitnessPal MCP is a free, open-source MCP server that connects MyFitnessPal to Claude, Cursor, VS Code, or any MCP client. Log meals by talking, search the food database with macros, track trends, and export your nutrition history, all against your real MyFitnessPal diary.

Published on PyPI as mfp-mcp and in the official MCP Registry as io.github.Mason-Levyy/mfp-mcp.

Unofficial. MyFitnessPal has no public API; this reverse-engineers the

web app's own endpoints. It can break whenever MFP changes their site. Use at

your own risk, with your own account.

Why this one?

MyFitnessPal moved behind Cloudflare + NextAuth, which broke the username/password login that most existing integrations rely on. This server:

  • Authenticates with your browser session cookie over a real Chrome TLS fingerprint (curl_cffi), which passes Cloudflare
  • Auto-refreshes the session (optional): a headless browser profile rotates the token when it expires, and failed calls retry automatically
  • Writes, not just reads: log, modify, and delete real diary entries
  • Search-then-log: get candidates with macros, then log the exact item

Quickstart

  1. Connect your account (one-time; prompts you to paste a cookie — see Authentication):
   uvx mfp-mcp auth
  1. Add the server to your client. The install buttons at the top do this in one click for Cursor and VS Code.

Claude Code

   claude mcp add myfitnesspal -- uvx mfp-mcp

Or install it as a plugin:

   /plugin install myfitnesspal-mcp --marketplace Mason-Levyy/myfitnesspal-mcp

Claude Desktop (claude_desktop_config.json), Cursor (~/.cursor/mcp.json), Windsurf (~/.codeium/windsurf/mcp_config.json), and Cline (cline_mcp_settings.json) all take the same entry:

   {

"mcpServers": { "myfitnesspal": { "command": "uvx", "args": ["mfp-mcp"] } } }

VS Code, Codex CLI, Gemini CLI

VS Code (or use the button above):

   code --add-mcp '{"name":"myfitnesspal","command":"uvx","args":["mfp-mcp"]}'

Codex CLI:

   codex mcp add myfitnesspal -- uvx mfp-mcp

Gemini CLI:

   gemini mcp add myfitnesspal uvx mfp-mcp
  1. Talk to it: *"log a banana as a snack"*, *"what did I eat yesterday?"*, *"chart my weight this month"*.

Requires uv. Any MCP client that speaks stdio or streamable HTTP works, not just Claude.

Finding this useful? A ⭐ on

GitHub helps other

MyFitnessPal users find it.

Authentication

This server signs in with your browser's MyFitnessPal session cookie:

  1. Log in at myfitnesspal.com
  2. Open DevTools (F12) → Application (Chrome) or Storage (Firefox) → Cookies → https://www.myfitnesspal.com
  3. Copy the value of __Secure-next-auth.session-token
  4. Paste it into the mfp-mcp auth prompt, or on macOS run mfp-mcp auth --clipboard and copy the cookie: it connects as soon as the token reaches the clipboard

Sessions last 30 days from their last renewal. The server renews the session whenever it builds a client and saves the cookies MyFitnessPal re-issues, so the saved session stays current as long as it's used at least once a month. mfp-mcp auth --check reports whether the saved session still works and whether auto-refresh is set up; it renews and saves the session as it checks and exits 1 when the session needs attention, so running it on a schedule (cron, launchd) keeps an idle session alive and warns before it lapses.

Auto-refresh (recommended)

With the autorefresh extra, auth also seeds a persistent headless browser profile. When MyFitnessPal rejects the session mid-call, the server tells your client it is retrying, boots the profile headlessly, lets MyFitnessPal rotate the session token, saves the fresh cookie, and retries the call.

uvx --from 'mfp-mcp[autorefresh]' playwright install chromium

uvx --from 'mfp-mcp[autorefresh]' mfp-mcp auth

Then use the same --from 'mfp-mcp[autorefresh]' form in your client config (e.g. uvx --from 'mfp-mcp[autorefresh]' mfp-mcp).

Tools

ToolWhat it does
fitness_get_dayNutrition totals, goals and remaining macros, diary entries, the MFP daily note, and feel note for a day
fitness_search_foodCandidate matches with brand, calories, macros, serving, and ids
fitness_draft_foodNumbered options with every serving size, filtered/ranked by optional calorie and macro targets
fitness_log_foodLog a draft option (and serving), a remembered food, or exact ids to the real diary
fitness_list_food_pinsRemembered query → food/serving choices (local)
fitness_clear_food_pinForget one remembered choice, or all of them
fitness_delete_foodRemove a diary entry by name match
fitness_modify_foodReplace an entry (or change its quantity), choosing the replacement like fitness_log_food
fitness_complete_dayReport whether a diary day is complete, mark it complete ("Complete This Entry"), or reopen it
fitness_log_weightLog a weight measurement (updates the same day on re-log)
fitness_log_waterAdd water (cups, fl oz, mL, L) or set the day's total with replace
fitness_get_exerciseRead the exercise diary (cardio + strength)
fitness_get_exercise_entriesList cardio and strength entries with each section's columns
fitness_delete_exerciseRemove an exercise entry by name match (all_matches for multi-row sync cleanup)
fitness_get_noteRead the MyFitnessPal daily diary note (the "Notes" box) for a day
fitness_log_noteWrite that daily note to MFP (replace, or append a new line)
fitness_log_feelSave a subjective "how I feel" note (stored locally, never sent to MFP)
fitness_get_trendsOne metric over a date range: weight, calories_in, protein, carbs, fat
fitness_bulk_exportWhole date range in one call, including goals and remaining macros
Logging flow

Food logging is draft-then-confirm, so what lands in your diary never depends on MyFitnessPal's search order:

  1. fitness_draft_food("greek yogurt", min_protein=15, max_calories=150) returns numbered options. Each lists every serving size with calories and macros for the whole entry (serving × quantity) plus a suggested serving; options that meet the targets come first, near misses follow. Call it again with different targets to refine.
  2. fitness_log_food(draft_id=..., option=2, serving=1) logs exactly that food and serving, and remembers the choice for that query.
  3. Next time, fitness_log_food(query="greek yogurt") logs the remembered food and serving without searching.

A bare fitness_log_food(query=...) with nothing remembered logs only when exactly one result matches the name exactly; otherwise it logs nothing and returns a draft to choose from. Remembered choices live in the local cache (fitness_list_food_pins, fitness_clear_food_pin). Drafts expire after 24 hours.

fitness_modify_food picks the replacement the same way, before deleting anything: a remembered food or a single exact-name match is used directly; otherwise the entry is left alone and a draft comes back — call fitness_modify_food again with the same query plus draft_id and option.

fitness_log_food, fitness_delete_food, and fitness_modify_food all take a meal argument the same way: breakfast/lunch/dinner/snacks, or the literal name of any meal section currently on your diary — including a renamed default meal or one of the up to two extra meals MyFitnessPal lets you add in Diary Settings. Matching is case-insensitive against your account's current meal labels. If you renamed a default meal, the keywords still work: breakfast/lunch/dinner/snacks fall back to your first through fourth meal, unless that slot now carries a different default name (e.g. lunch won't land in a second meal you renamed to "Dinner"). Any other unrecognized meal raises an error instead of silently logging into the wrong section.

Day summaries and trends read from a local SQLite cache that gap-fills from MyFitnessPal (first call on a fresh install fetches up to 30 days, one request per day).

Water logging reads the day's current total from /food/water, adds the requested quantity, and posts the new total back to the same endpoint. Pass replace=True to set the day's total outright (e.g. to fix a mislogged amount); every call returns previous_ml, so a mistake can be reverted by passing it back as amount with unit="ml" and replace=True. The separate /stats request with a water_logged event is analytics telemetry; it does not persist the water total.

Remote / HTTP mode

The default transp

Only the beginning is shown; read the rest in the repo

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