meal-diary-mcp

by Aniruddha ChowdhuryUpdated May 4, 2026

Converts unstructured food diary text into structured meal records, nutritional estimates, weekly summaries, and dietary pattern flags. Developers integrate it into health apps to parse user logs automatically. Nutritionists and fitness trackers use it for analyzing eating habits and generating reports.

nutrition-analysis
diet-tracking
food-parsing
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Overview

The meal-diary-mcp server processes raw text from food diaries, extracting and structuring meal data, estimating nutritional values, compiling weekly summaries, and identifying patterns like excessive sugar intake or irregular meal timing. It uses MCP protocols to handle natural language inputs from logs, journals, or app entries, outputting JSON-formatted data for further analysis or storage.

Key Capabilities

  • Structured meals extraction: Parses text to identify meals, ingredients, portions, and timestamps, outputting as structured objects with fields like meal_type, ingredients, and estimated_portion.
  • Nutritional estimates: Calculates approximate calories, macros (proteins, carbs, fats), and micros based on common food databases, providing fields like calories, protein_g, and confidence_score.
  • Weekly summaries: Aggregates daily data into weekly overviews, including total intake, averages, and trends like avg_daily_calories or total_protein.
  • Pattern flags: Detects anomalies such as high_sugar_week, skipped_meals, or high_sodium_days, with explanations and severity levels.

Use Cases

  1. Fitness app integration: Feed user-submitted diary text into the server to generate structured data for dashboards showing weekly calorie trends and pattern flags for coaching alerts.
  2. Nutrition coaching: Process client journals to produce summaries and flags, enabling coaches to review high_sugar_week patterns and adjust meal plans.
  3. Personal health tracking: In a journaling app, automatically structure logs and flag issues like skipped_meals for user notifications and long-term progress reports.
  4. Research data prep: Convert bulk text diaries from studies into analyzable JSON for statistical analysis of dietary patterns across groups.

Who This Is For

Developers building diet-tracking apps or health platforms; nutritionists and dietitians analyzing client logs; researchers handling food intake data; individuals or teams maintaining personal or group meal diaries needing automated insights.