$ loading_
Store personal health data locally for AI logging, retrieval, and analysis.
Copy the install command and let the AI configure it · recommended for beginners
No copy-paste install info for "HealthLedger MCP" yet — see the docs or source repo.
Please log today's health record to HealthLedger MCP: weight 68.2 kg, resting heart rate 62, sleep 7.5 hours, note: felt good after a 5 km morning run.
The tool stores a structured health record for later querying and analysis.
Retrieve my sleep duration and resting heart rate records from the last 14 days in HealthLedger MCP, organized by date.
Returns the relevant health records organized by date for trend review.
Using the last 30 days of records in HealthLedger MCP, analyze whether my sleep, weight, and resting heart rate show clear trends, and provide a brief summary.
Produces a trend summary based on stored data to help the user understand recent changes.
Individuals tracking their health can use any AI client to continuously write daily metrics, sleep data, or notes into a local ledger. This centralizes records and makes later retrieval easier.
Researchers or data analysts can use the analysis-ready views to read saved health records and have AI summarize or reason over them. It is suitable for reviewing changes over time.
Users who prefer to keep sensitive information local can use this local-first tool to store personal health data in a SQLite file. This avoids depending on a specific model or cloud storage approach.
It is a local-first, model-agnostic MCP server that stores personal health data in a SQLite file and provides views for AI clients to log, retrieve, and analyze records.
Based on the description, no. It is described as model-agnostic, meaning it can work with any AI client rather than being tied to one specific model.
The description says the data is stored in a local SQLite file. For the exact file location, schema, or installation steps, see the source repository.
Sync workouts and nutrition data through one MCP interface for analysis.
Aggregate and query personal health data across sources with attribution.
Query Google Health fitness data in natural language for quick personal insights.
Query and export Apple Health data with trends, comparisons, and structured results.
A personal assistant MCP server for calendars, tasks, and wellness logging.
Query patient records safely and analyze risk and readmission with AI.