Personal Agent Glycemic Load Macro Balancer
Click on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@Personal Agent Glycemic Load Macro BalancerEstimate the glycemic load and crash risk for my lunch: 60g carbs, 10g protein, 5g fat."
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
genpark-personal-agent-glycemic-load-macro-balancer-skill
GenPark Personal Agent Glycemic Load Macro Balancer computes composite glycemic impact, models protein and fat gastric attenuation, and forecasts post-prandial mental stamina without external dependencies.
flowchart TD
A[Meal Ingredients, Carbs & GI] --> B[Composite Glycemic Load Calculator]
C[Protein & Healthy Fat Amounts] --> D[Gastric Emptying Buffering Index]
B & D --> E[Effective Attenuated Glycemic Load]
E --> F[Post-Prandial Brain Fog & Crash Probability]
F --> G[Cognitive Focus Hours & Task Scheduling Advice]Features
Composite Glycemic Load: Aggregates glycemic index and carbohydrate mass across complex multi-ingredient meals.
Macronutrient Damping: Models how amino acids and lipids flatten glycemic glucose spikes.
Cognitive Crash Forecaster: Estimates post-lunch fatigue probability and stable working hours.
Related MCP server: Dieta.ai
Quickstart
from client import PersonalAgentGlycemicMacroBalancer
balancer = PersonalAgentGlycemicMacroBalancer()
res = balancer.recommend_macronutrient_stabilizer(40.0)
print("Recommended Protein:", res["recommended_protein_addition_g"], "g")FastMCP Integration
python -m genpark-personal-agent-glycemic-load-macro-balancer-skill.mcp_serverLicense
MIT
This server cannot be deployed
Maintenance
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