Sleep Tracker AI MCP

What sleep-tracker-ai-mcp MCP server does

The sleep-tracker-ai-mcp MCP server provides an MCP interface for recording and reviewing sleep information. Its documented purpose is to track sleep duration and quality, calculate or return sleep statistics, identify patterns, and provide personalized sleep recommendations. The repository describes three example tools: log_sleep, get_sleep_stats, and analyze_patterns.

This fits applications where a conversational assistant should help a user maintain sleep records and inspect trends through tool calls. The provided material does not describe a graphical interface, wearable-device integration, a hosted database, or connections to external health services.

How it works

The server follows the Model Context Protocol and is intended to be launched as a local stdio process. An MCP client starts the sleep-tracker-ai-mcp executable, then makes the available sleep-related operations accessible to an assistant. The README shows example requests that explicitly invoke log_sleep, get_sleep_stats, and analyze_patterns.

The documented workflow is to add sleep data, request summary statistics, and analyze recorded patterns. Recommendations are listed as part of the project’s purpose, although the excerpt does not define the recommendation algorithm, input schema, output schema, or persistence behavior. Those details should be checked in the linked documentation or API reference before building a production workflow around them.

Setup and configuration

The sleep-tracker-ai-mcp MCP server can be installed from PyPI with:

pip install sleep_tracker_ai_mcp

The README also provides a direct uvx configuration for MCP clients:

{
  "mcpServers": {
    "sleep-tracker-ai-mcp": {
      "command": "uvx",
      "args": ["sleep-tracker-ai-mcp"]
    }
  }
}

After installing the package, the executable can also be run directly with sleep-tracker-ai-mcp. The README identifies this as stdio transport. It specifically mentions Claude Desktop configuration, but does not document settings for other clients in the supplied material. No API key, token, account, or environment variable is listed as required.

Tools and capabilities

The documented capabilities are:

• log_sleep: records sleep information; the excerpt does not define its required fields.
• get_sleep_stats: retrieves sleep statistics.
• analyze_patterns: analyzes recorded sleep patterns.
• Sleep duration and quality tracking.
• Personalized sleep recommendations, as stated in the project description.

The repository describes the implementation as MCP protocol compliant and includes links to separate documentation and an API reference. Tool parameters, return formats, storage duration, date handling, and error behavior are not included in the provided README excerpt.

Limitations and notes

The available material is not sufficient to verify whether records persist between runs, whether multiple users are supported, or whether the server accepts data from sleep trackers or wearable devices. It also does not establish that recommendations are medical advice; applications should avoid presenting them as a substitute for professional care without additional validation.

The project is published under the MIT license. The README also advertises paid enterprise offerings and governance products from the wider organization, but those offerings are separate from the local sleep-tracker-ai-mcp MCP server installation described here.