Calorie Tracker MCP Server

The Calorie Tracker MCP Server is a lightweight, cloud‑hosted service that empowers AI assistants to provide personalized nutrition insights and habit tracking. By exposing a suite of tools for food logging, profile management, and health analytics, it turns raw dietary data into actionable guidance that can be delivered directly to users through conversational agents. The server’s architecture—built on Cloudflare Workers with a D1 database—ensures low latency, global reach, and cost‑effective scaling while keeping sensitive health information securely isolated in a KV namespace.

At its core, the MCP solves the problem of fragmented nutrition data. Users often rely on multiple apps or spreadsheets to monitor calories, macronutrients, and body metrics, which makes it difficult for an AI assistant to maintain a consistent view of progress. The Calorie Tracker MCP consolidates these inputs into a single source of truth, automatically computing Basal Metabolic Rate (BMR) and Total Daily Energy Expenditure (TDEE) using the Mifflin‑St Jeor equation. These calculations become first‑class data points that an assistant can reference when recommending meal plans, adjusting caloric goals, or explaining weight trends. Because the server handles authentication via OAuth 2.1 with PKCE support, developers can integrate it into existing identity flows without exposing credentials or storing passwords.

Key capabilities include a rich set of tools: , , and let users manage individual food logs; supports pagination for long histories. Profile tools such as , , and provide a holistic view of body composition, activity level, and calculated energy needs. Administrative endpoints (, ) enable multi‑tenant deployments where teams can onboard new members or revoke access on demand. The server’s design also accommodates both bearer‑token and OAuth client‑credential authentication, giving developers flexibility to choose the model that best fits personal or enterprise environments.

Real‑world scenarios for this MCP are abundant. A health coach can embed the server in a chatbot that delivers daily meal suggestions based on logged intake and current BMR/TDEE. A fitness app might expose the MCP to a virtual assistant that tracks progress, alerts users when they exceed caloric thresholds, or recommends adjustments after a workout. In corporate wellness programs, the server can aggregate anonymized data across employees to generate insights while preserving privacy through OAuth scopes. Because all interactions are performed via MCP tools, developers can compose complex workflows—such as prompting a user to log a meal, then immediately calculating the impact on their daily goal—all within a single conversational turn.

What sets this MCP apart is its seamless integration with Claude’s Model Context Protocol. The server exposes a stable, typed interface that the assistant can call without custom adapters; OAuth flows are fully supported, ensuring secure, user‑specific data access. The use of Cloudflare Workers guarantees that responses are delivered from the nearest edge location, minimizing latency for users worldwide. Together, these features provide developers with a powerful, turnkey solution to bring nutrition science into everyday AI interactions.