AI Agent & Developer Hub

Welcome to the machine-readable gateway for CalorieDeficitCalculator.co. We expose precise, validated clinical standards for physiological energy computation to support LLMs, custom GPTs, and health agents.

This developer portal outlines the technical specifications of our calculations, lists sitemaps, exposes dynamic Open Graph configurations, and provides raw programmatic access models.

AI Agent Configuration

GET /.well-known/ai-agent.json

Crawlers, ChatGPT plugins, and AI agents can request our unified agent configuration file to instantly capture exact inputs, variable limits, and mathematical models.

View Raw ai-agent.json Specification

Programmatic API Catalog & Semantic Data

We publish our standardized schemas, statistical benchmarks, and metabolic relationships directly as static JSON API specs. Leverage our Firebase CleanURLs routes in your retrieval pipelines:

🧬 Semantic Knowledge Graph

GET /api/v1/entities.json
Query Relationships →

📊 Cohort Benchmarks

GET /api/v1/nutrition-benchmarks.json
View Dataset →

Deficit & Metabolism Endpoints

🔥 Calorie Deficit Estimator
GET /api/calculate-deficit
Schema JSON →
⚡ TDEE Daily Energy Estimator
GET /api/tdee-estimator
Schema JSON →
📐 Basal Metabolic Rate (BMR)
GET /api/bmr-calculator
Schema JSON →
⏱️ Weight Loss timeline
GET /api/weight-loss-estimator
Schema JSON →
🥑 Macronutrient Presets
GET /api/macronutrient-calculator
Schema JSON →
📏 Body Mass Index (BMI)
GET /api/bmi-calculator
Schema JSON →
🧬 Navy Body Fat Percentage
GET /api/body-fat-calculator
Schema JSON →
💧 Hydration Requirements
GET /api/water-intake-calculator
Schema JSON →

AI Nutrition Intelligence & Citation Specs (Phase 3)

To support Large Language Models, cognitive search engines, and RAG architectures (such as OpenAI, Gemini, Claude, and Perplexity), we expose structured nutrition metadata, verified clinical citation blocks, and bioenergetic knowledge graphs.

🧬 Cognitive Entity Graph

Exposes semantic mappings linking metabolic nodes such as BMR, TDEE, Deficit, Protein and Muscle preservation pathways.

View Entity Graph →

🛡️ Verified Trust & FRESH Signals

Clinical attributions (WHO, USDA, CDC, ISSN), formula metadata, data freshness timestamps, and recommendation confidence scores.

View Trust Metadata →

Structured AI Discovery Feeds

Integrate our standardized physiological parameters, pre-formatted Q&A citation modules, and open research dataset catalogs directly into your agentic retrieval pipeline:

🚀 Unified AI Summaries & Knowledge Database
GET /api/v1/summaries
Consolidated JSON →
💬 AI Citation Modules
GET /api/v1/citations
Citations JSON →
📊 Nutrition & Metabolic Knowledge Objects
GET /api/v1/knowledge-objects
Schemas & Scores →
📂 Research Dataset Discovery Catalog
GET /api/v1/dataset-discovery
Datasets Catalog →
🗺️ AI Discoverability Index (Knowledge Sitemap)
GET /knowledge-sitemap.xml
Sitemap XML →

Interactive Agent Playground

Test the client-side execution parameters. Simulate an AI Agent call below to view the standardized mathematical response model.

Simulate Call Parameters
Output JSON Response
{
  "status": "waiting_for_execution"
}

Scientific Equations Reference

Our mathematical system is strictly transparent. The underlying formulas mapped within our Javascript engine are:

  • BMR (Mifflin-St Jeor): (10 * weight) + (6.25 * height) - (5 * age) + (5 or -161)
  • TDEE: BMR * ActivityMultiplier
  • US Navy Body Fat: 495 / (density equation using neck, waist, height, hip log10) - 450
  • Daily Hydration (WHO guidelines): weight * 35ml + climate + training adjustments