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Senechal MCP Server: Seamless Collaboration & Maximum Performance - MCP Implementation

Senechal MCP Server: Seamless Collaboration & Maximum Performance

Power your Senechal project with this seamless MCP server—built for effortless collaboration and peak performance. The backbone your team needs.

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About Senechal MCP Server

What is Senechal MCP Server: Seamless Collaboration & Maximum Performance?

Senechal MCP Server is a middleware solution designed to streamline integration between AI language models and health data systems. It acts as a bridge, enabling LLM applications to efficiently retrieve, analyze, and utilize health metrics through standardized resource endpoints and tool APIs. Built for performance and interoperability, it ensures seamless collaboration between healthcare data sources and AI-driven analytical workflows.

How to Use Senechal MCP Server: Seamless Collaboration & Maximum Performance?

  1. Setup Configuration: Install the server and configure API credentials with the Senechal health data platform.
  2. Resource Access: Use URIs like senechal://health/summary to fetch structured health data directly into LLM sessions.
  3. Tool Invocation: Trigger predefined tools via API calls (e.g., fetch_health_trends) to retrieve trend analysis over specified periods.
  4. Prompt Integration: Leverage pre-built analysis templates (e.g., analyze_health_summary) to generate actionable insights from raw data.

Senechal MCP Server Features

Key Features of Senechal MCP Server: Seamless Collaboration & Maximum Performance?

  • Health Data Abstraction: Unified access to patient metrics, vitals, and longitudinal records.
  • Performance-Optimized APIs: Low-latency endpoints for real-time analytics and batch processing.
  • Pre-Built Analytical Tools: Out-of-the-box functions for statistical analysis, trend detection, and anomaly scoring.
  • Security Frameworks: Role-based access control and HIPAA-compliant data handling.
  • LLM Native Integration: Direct support for popular AI platforms via standardized query protocols.

Use Cases: Leveraging Senechal MCP for Healthcare AI

  • Proactive Patient Monitoring: Automated risk stratification using vital trends and lab results.
  • Clinical Decision Support: Real-time analysis of EHR data to suggest evidence-based interventions.
  • Research Pipelining: Batch processing of de-identified datasets for epidemiological studies.
  • Telehealth Applications: Context-aware symptom analysis paired with patient history.

Senechal MCP Server FAQ

FAQ: Senechal MCP Server Best Practices

Q: How is data secured during transmission?
All communications use TLS 1.3 encryption with mutual certificate authentication.

Q: What is the maximum query throughput?
Configurable rate limiting supports up to 1500 requests/sec with proper scaling architecture.

Q: Can I customize analysis templates?
Yes - the /prompts API allows organizations to deploy domain-specific analysis configurations.

Q: How is patient privacy maintained?
Data access is governed by role-based permissions and audit trails, with optional anonymization pipelines for research use.

Content

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
  1. 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

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