Apple Health MCP Server

The Apple Health MCP Server turns the wealth of data captured by iOS devices into a conversational knowledge base that any large‑language model can interrogate. By exposing Apple Health XML exports through a lightweight, high‑performance MCP interface built on FastMCP, the server removes the need for custom parsers or manual database migrations. Developers can import their health records into a choice of back‑ends—Elasticsearch, ClickHouse, or DuckDB—and then let an LLM ask questions in plain English. The server translates those natural‑language queries into efficient search, filtering, and aggregation operations behind the scenes.

At its core, the server solves two common pain points for health‑tech developers: (1) data ingestion—converting the nested XML format Apple Health uses into a searchable schema—and (2) data access—providing an intuitive, language‑agnostic API that any MCP‑enabled assistant can call. The result is a plug‑and‑play analytics layer: once the data lives in the chosen database, any AI assistant can generate summaries, spot trends, or even suggest workouts without developers writing SQL or custom code.

Key capabilities include:

• Natural‑language querying that automatically maps user intent to database filters, enabling non‑technical stakeholders to explore metrics like daily steps or sleep patterns.
• Auto‑generated statistical summaries such as weekly activity overviews, monthly running distances, or heart‑rate trends that surface insights humans might miss.
• Modular MCP tools for schema inspection, record retrieval by type, and targeted extraction, giving developers fine‑grained control over the data exposed to the assistant.
• Flexible back‑end support (Elasticsearch for full‑text search, ClickHouse for columnar analytics, DuckDB for lightweight embedded queries) so teams can pick the engine that matches their scale and latency needs.
• Docker‑ready deployment for rapid onboarding in CI/CD pipelines or cloud environments.

Real‑world use cases abound: a wellness app could let users ask “How did my sleep change last month?” and receive an instant, data‑driven answer; a health researcher could query “What is the average daily step count for users over 50?” without writing a query; or an AI‑powered coaching assistant could surface personalized workout suggestions based on historical activity trends. Because the server abstracts both ingestion and query logic, integration into existing AI workflows is as simple as pointing an MCP‑enabled model at the server’s endpoint—no additional code required.

In short, the Apple Health MCP Server empowers developers to unlock the narrative hidden in raw health data, turning it into a conversational resource that scales with any LLM and adapts to the chosen analytics stack.