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Senechal MCP Server

by mattjoyce
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Python
GPL 3.0
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remote-capable server

The server can be hosted and run remotely because it primarily relies on remote services or has no dependency on the local environment.

Integrations

  • Supports environment variable configuration through .env files for storing API keys and base URLs required to connect to the Senechal health data API.

  • Uses Python for implementing the server, with specific instructions for creating virtual environments and running the server through Python commands.

Senechal MCP Server

A Model Context Protocol (MCP) server that acts as a companion to the Senechal project, providing health data from the Senechal API to LLM applications.

Overview

This server provides a standardized interface for LLMs to access health data from the Senechal API. It exposes:

  • Resources: Health data that can be loaded into an LLM's context
  • Tools: Functions that can be called by LLMs to fetch health data
  • Prompts: Reusable templates for analyzing health data

Installation

  1. Clone this repository
  2. Create a virtual environment:
    python -m venv venv source venv/bin/activate # On Windows: venv\Scripts\activate
  3. Install dependencies:
    pip install -r requirements.txt

Configuration

Copy the .env.example file to .env and add your Senechal API key and URL:

# Required: Senechal API Key SENECHAL_API_KEY=your_api_key_here # Required: API base URL SENECHAL_API_BASE_URL=https://your-api-host/api/senechal

Both the API key and API URL are required for the server to function.

Windows Configuration

When running on Windows, be sure to:

  1. Use backslashes or properly escaped paths in the configuration
  2. Use the full path to your Python virtual environment in the claude-desktop-config.json:
{ "mcpServers": { "senechal-health": { "command": "C:\\path\\to\\venv\\Scripts\\python.exe", "args": [ "C:\\path\\to\\senechal_mcp_server.py" ], "env": { "SENECHAL_API_KEY": "your_api_key_here" } } } }

Note that environment variables in the MCP configuration do not use the .env file, so you'll need to set them explicitly in the config.

Usage

Testing the Client/Server Setup

The simplest way to test the setup is to run the example client:

# In one terminal, start the server python senechal_mcp_server.py # In another terminal, run the example client python example_client.py

Start the Server

python senechal_mcp_server.py

Development Mode with MCP Inspector

mcp dev senechal_mcp_server.py

Install in Claude Desktop

The server includes a configuration file for Claude Desktop:

mcp install senechal_mcp_server.py

You can then select "Senechal Health" from the tools menu in Claude Desktop.

Available Resources

  • senechal://health/summary/{period} - Get health summary for day, week, month, or year
    • Example: senechal://health/summary/day?span=7&metrics=all
    • Parameters:
      • period: day, week, month, year
      • span: Number of periods (default: 1)
      • metrics: Comma-separated list or "all" (default)
      • offset: Number of periods to offset from now (default: 0)
  • senechal://health/profile - Get the user's health profile
    • Contains demographics, medications, supplements
  • senechal://health/current - Get current health measurements
    • Example: senechal://health/current?types=1,2,3
    • Parameters:
      • types: Optional comma-separated list of measurement type IDs
  • senechal://health/trends - Get health trends over time
    • Example: senechal://health/trends?days=30&types=1,2,3&interval=day
    • Parameters:
      • days: Number of days to analyze (default: 30)
      • types: Optional comma-separated list of measurement type IDs
      • interval: Grouping interval - day, week, month (default: day)
  • senechal://health/stats - Get statistical analysis of health metrics
    • Example: senechal://health/stats?days=30&types=1,2,3
    • Parameters:
      • days: Analysis period in days (default: 30)
      • types: Optional comma-separated list of measurement type IDs

Available Tools

  • fetch_health_summary - Fetch a health summary for a specific period
    • Parameters:
      • period (required): day, week, month, year
      • metrics (optional): Comma-separated metrics or "all" (default)
      • span (optional): Number of periods to return (default: 1)
      • offset (optional): Number of periods to offset (default: 0)
  • fetch_health_profile - Fetch the user's health profile
    • No parameters required
  • fetch_current_health - Fetch the latest health measurements
    • Parameters:
      • types (optional): List of measurement type IDs to filter by
  • fetch_health_trends - Fetch health trend data
    • Parameters:
      • days (optional): Number of days to analyze (default: 30)
      • types (optional): List of measurement type IDs to filter by
      • interval (optional): Grouping interval - day, week, month (default: day)
  • fetch_health_stats - Fetch statistical analysis of health metrics
    • Parameters:
      • days (optional): Analysis period in days (default: 30)
      • types (optional): List of measurement type IDs to filter by

Available Prompts

  • analyze_health_summary - Prompt to analyze health summaries
    • Provides a template for identifying abnormal metrics, trends, and suggesting actions
    • Intended to be used with data from senechal://health/summary/day?span=7
  • compare_health_trends - Prompt to compare health trends over different time periods
    • Provides a template for comparing trends across different timeframes (7, 30, 90 days)
    • Intended to be used with data from the health trends endpoint

Example Interactions

Loading Health Summary Data

# In an LLM application, load a week of health summaries content, mime_type = await session.read_resource("senechal://health/summary/day?span=7")

Calling Health Data Tools

# In an LLM conversation result = await session.call_tool( "fetch_health_trends", arguments={ "days": 30, "interval": "day" } ) # More complex example combining tools and resources profile = await session.call_tool("fetch_health_profile") trends = await session.call_tool( "fetch_health_trends", arguments={"days": 90, "interval": "week"} )

Using Health Analysis Prompts

# Get a prompt for analyzing health data prompt_result = await session.get_prompt("analyze_health_summary") for message in prompt_result.messages: print(f"[{message.role}]: {message.content.text}")

See the example_client.py file for a complete working example.

API Endpoints

The Senechal MCP server communicates with the following Senechal API endpoints:

  • /health/summary/{period} - Get health summaries
  • /health/profile - Get health profile
  • /health/current - Get current measurements
  • /health/trends - Get health trends
  • /health/stats - Get health stats

This server cannot be installed

-
security - not tested
A
license - permissive license
-
quality - not tested

A Model Context Protocol server that provides health data from the Senechal API to LLM applications, enabling AI assistants to access, analyze, and respond to personal health information.

  1. Overview
    1. Installation
      1. Configuration
        1. Windows Configuration
        2. Usage
          1. Testing the Client/Server Setup
            1. Start the Server
              1. Development Mode with MCP Inspector
                1. Install in Claude Desktop
                2. Available Resources
                  1. Available Tools
                    1. Available Prompts
                      1. Example Interactions
                        1. Loading Health Summary Data
                          1. Calling Health Data Tools
                            1. Using Health Analysis Prompts
                            2. API Endpoints

                              Related Resources