About Senechal MCP Server
Senechal MCP Server connects LLM applications to health data from the Senechal API. It provides resources, tools, and reusable prompts for retrieving summaries, measurements, trends, and statistics.
Key features
- 01Read health summaries and profiles
- 02Retrieve current health measurements
- 03Query health trends and statistics
- 04Provide health analysis prompts
Use cases
- 01Load periodic health summaries into an LLM app
- 02Retrieve selected current measurements
- 03Analyze health trends across time periods
- 04Use prompts to analyze health metrics
MCP support
The MCP server exposes Senechal API health data as resources and tools, and provides prompts for health data analysis.
Configuration
Local server config is the same for every client. Copy it into your client’s MCP config file.
We haven’t collected a package or remote endpoint for this server. Follow the instructions in the project README.
Tools
Local servers expose tools only once running on your machine, and the maintainer has not provided a tool list.
Details
From the project READMESenechal 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
- Clone this repository
- Create a virtual environment:
python -m venv venv source venv/bin/activate # On Windows: venv\Scripts\activate - 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:
- Use backslashes or properly escaped paths in the configuration
- 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, yearspan: Number of periods (default: 1)metrics: Comma-separated list or "all" (default)offset: Number of periods to offset from now (default: 0)
- Example:
-
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
- Example:
-
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 IDsinterval: Grouping interval - day, week, month (default: day)
- Example:
-
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
- Example:
Available Tools
-
fetch_health_summary- Fetch a health summary for a specific period- Parameters:
period(required): day, week, month, yearmetrics(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)
- Parameters:
-
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
- Parameters:
-
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 byinterval(optional): Grouping interval - day, week, month (default: day)
- Parameters:
-
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
- Parameters:
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
Changelog
See CHANGELOG.md.
Comments
New to MCP?
From what the protocol is to installing your first server, these answers cover most first questions.
An open protocol that defines how AI applications connect to external tools and data in a uniform way. Clients talk to the model; MCP servers provide tools, resources and prompt templates. They exchange standard messages, so one server can be reused by many clients.