Consulta datos de Apple Health desde un cliente MCP usando SQL y DuckDB. El servidor se ejecuta localmente, lee exportaciones CSV bajo demanda y proporciona herramientas para descubrimiento de esquemas, consultas de solo lectura y resúmenes de salud.
Node.js 22 o más reciente
Una exportación CSV de Apple Health creada con Simple Health Export CSV
El formato nativo export.xml de Apple Health no se admite actualmente.
Related MCP server: apple-mcp
Para Claude Desktop, añade lo siguiente a
~/Library/Application Support/Claude/claude_desktop_config.json:
{
"mcpServers": {
"apple-health": {
"command": "npx",
"args": ["-y", "@neiltron/apple-health-mcp"],
"env": {
"HEALTH_DATA_DIR": "/path/to/your/unzipped/health-export"
}
}
}
}Reinicia el cliente después de cambiar su configuración. Otros clientes MCP pueden usar el mismo comando, argumentos, entorno y transporte stdio.
Variable | Obligatoria | Por defecto | Propósito |
| Sí | — | Directorio que contiene los archivos CSV exportados |
| No |
| Límite de memoria de DuckDB en megabytes |
| No |
| Número máximo de resultados de consulta en caché |
Instala y abre Simple Health Export CSV en tu iPhone.
Selecciona Todo y elige el rango de tiempo a exportar.
Transfiere el archivo al ordenador que ejecuta tu cliente MCP.
Descomprímelo y establece HEALTH_DATA_DIR al directorio resultante.
El servidor lee los archivos en su lugar. No sube la exportación ni realiza solicitudes de red, aunque los resultados de consulta devueltos a tu cliente MCP pueden enviarse al proveedor de modelos configurado de ese cliente.
Herramienta | Propósito |
| Descubre nombres de tablas, columnas, unidades y filas de ejemplo |
| Ejecuta una consulta |
| Genera un resumen de salud semanal, mensual o personalizado |
Comienza con health_schema; los nombres de las tablas dependen de los archivos de tu exportación. Consulta Querying Apple Health data para el modelo de datos y ejemplos prácticos.
La primera solicitud que necesita una tabla carga el historial CSV completo de esa tabla. No hay ventana de fechas, por lo que una consulta puede llegar tan atrás como llegue la exportación.
Debido a que cada herramienta puede acceder a todo el historial configurado, solo inicia este servidor desde un cliente MCP en el que confíes con esos datos.
Las tablas cargadas se mantienen en memoria, y a DuckDB se le da el límite MAX_MEMORY_MB descrito anteriormente. Aproximadamente 1 GiB cubre una exportación de dos años con múltiples tablas, por lo que el valor predeterminado de 2048MB deja margen; aumenta MAX_MEMORY_MB para una exportación más grande. El servidor nunca vuelca filas de salud a un directorio temporal en disco, por lo que una exportación que no quepa en el límite falla con un error explícito en su lugar.
Otras limitaciones actuales:
Solo se admite el diseño CSV de Simple Health Export.
La base de datos DuckDB está en memoria y se reconstruye para cada proceso del servidor, por lo que cada inicio recarga desde los archivos CSV. La importación incremental persistente es trabajo futuro planificado, no comportamiento actual.
La superposición de dispositivos puede producir mediciones que parecen duplicadas; las consultas deben tener en cuenta sourceName cuando corresponda.
Los resúmenes de salud resumen datos registrados y no son consejo médico.
git clone https://github.com/neiltron/apple-health-mcp.git
cd apple-health-mcp
bun install
npm test
npm run typecheck
npm run buildConsulta Architecture para la estructura del código, el ciclo de vida de los datos y las restricciones de implementación.
MIT
Run one analytical query on Apple Health data.
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes | One DuckDB analytical query. Joins, CTEs, subqueries, set operations, and window functions are supported. | |
| format | No | Output format (default: json) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must carry the behavioral burden. It states the tool runs a query but does not explicitly disclose that it is read-only, whether there are side effects, limits on query complexity or result size, or what the tool returns. 'Analytical query' weakly implies a read operation, but this is under-specified for a tool that accepts arbitrary SQL.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single front-loaded sentence with no filler: it names the verb, resource, and scope immediately. It is concise, but so terse that it misses opportunities to add routing or behavioral context that would make it more genuinely helpful.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a tool accepting arbitrary DuckDB SQL over health data, an agent needs to know how to discover available tables and fields IAW health_schema, and what output shape or limits to expect. With no output schema, no annotations, and no mention of health_schema, the description is only minimally viable.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema already documents the query string and the format enum including the default JSON value. The description adds the 'one analytical query' framing but no additional parameter detail beyond the schema, so the baseline 3 applies.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses a specific verb ('Run') and names the resource ('Apple Health data'), with the qualifier 'one analytical query.' It clearly communicates what the tool does, though it does not explicitly distinguish itself from the sibling tools health_report and health_schema.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Usage is implied: use this tool when an analytical query needs to be executed against Apple Health data. However, there is no explicit guidance about when to choose this tool over health_report or health_schema, and no mention of alternatives or exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Generate structured health reports for a specific period
| Name | Required | Description | Default |
|---|---|---|---|
| end_date | No | End date for custom reports (YYYY-MM-DD) | |
| start_date | No | Start date for custom reports (YYYY-MM-DD) | |
| report_type | Yes | Type of report to generate | |
| include_metrics | No | Metrics to include (default: all) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It only says 'Generate structured health reports' without disclosing whether the operation is read-only, what permissions are needed, what side effects exist, or what the output format is. This is insufficient for a tool with no output schema.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single sentence with no filler or redundant information. It is front-loaded with the action and object, making it easy to scan.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given there is no output schema and no annotations, the description is too minimal to provide complete context. It does not specify what a health report contains, how it is structured, or any edge-case behavior. The presence of sibling tools and moderate parameter count (4) calls for more explanatory content.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the baseline is 3. The description adds minimal value beyond the schema—it merely echoes the 'specific period' concept already covered by start_date and end_date descriptions. It does not explain report_type enum semantics or include_metrics default behavior.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description states a specific action ('Generate') and resource ('structured health reports') with a scope ('for a specific period'). It is clear what the tool does, though it does not explicitly distinguish itself from sibling tools like health_query or health_schema beyond the word 'report'.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Usage context is implied by the description: it generates reports for a period. However, there is no explicit guidance on when to use this tool versus siblings, nor any alternative or exclusion criteria.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Get information about available health data tables, their structure, and sample data to help write SQL queries
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the burden. It indicates a read-only, informational operation ('Get information'), which is consistent with a schema tool. However, it does not disclose any behavioral details such as data source, freshness, or access restrictions beyond the basic read-only implication.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, concise sentence that front-loads the action ('Get information') and immediately specifies the resource and purpose. Every phrase adds value with no redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple, parameterless tool, the description is largely complete. It specifies what the tool returns (table structures and sample data) and why it is useful (writing SQL queries). Without an output schema, this baseline description suffices.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema is empty with zero parameters, so there are no parameter semantics to explain. Per the baseline for 0-parameter tools, this scores 4.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Get information about available health data tables, their structure, and sample data.' It uses a specific verb ('get') and a specific resource, and the context of sibling tools (health_query, health_report) makes it distinct as the metadata/schema exploration tool.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage before writing SQL queries ('to help write SQL queries'), but it does not explicitly state when to use this tool versus health_query or health_report, nor does it mention any exclusions or alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
v1.4.5health_query1 field changedInput schema / properties / query / descriptionPrevious value: -"SQL SELECT query to execute"New value: +"One DuckDB analytical query. Joins, CTEs, subqueries, set operations, and window functions are supported."v0.1.0health_queryhealth_reporthealth_schemaScored across 3 tools
Each tool serves a clearly distinct purpose: health_query runs analytical queries, health_report generates reports, and health_schema provides metadata about tables. There is no overlap in functionality.
All tools follow a consistent pattern with the prefix 'health_' followed by a clear noun (query, report, schema). Naming is uniform and predictable.
With only 3 tools, the set is compact but well-scoped for the stated analytical purpose. While slightly minimal, each tool earns its place and doesn't feel overly sparse.
The tools cover the essential analytical workflow: querying data, generating reports, and understanding schema. Minor gaps exist (e.g., no direct data export), but for the apparent read-only analytical scope, coverage is sufficient.
Query, join, profile, clean and convert CSV/JSON/Parquet with server-side DuckDB over MCP.
MCP server for querying and analyzing data from ad platforms, analytics tools, and spreadsheets
GibsonAI MCP server: manage your databases with natural language
Text-to-SQL MCP server: read-only queries on PostgreSQL, MySQL and SQL Server with your real schema
