Provide MCP info for AI systems?

[benhammondmusic]

MCP is emerging as a standardized way for AI agents to access external data. Since our site provides valuable health equity data, calculations, and insights, we should discuss making this more accessible to AI systems which in turns makes it reach a broader audience.

From Claude:

Understanding MCP Protocol for AI Data Consumption

The Machine Consumption Protocol (MCP) is a structured data format designed to help AI systems efficiently consume and understand web content. It's essentially a standardized way to present your data that makes it more accessible to AI systems while requiring minimal changes to your existing infrastructure.

For your Health Equity Data web app, implementing MCP could offer several benefits:

1. Enhanced AI accessibility: AI systems could more accurately interpret your health equity visualizations and reports without misunderstandings.

2. Improved data interpretation: The protocol would help AI systems correctly understand demographic breakdowns, statistical significance, and contextual factors in health disparities.

3. Wider reach: Your important health equity information could be utilized by various AI assistants in answering questions about health disparities.

4. Data integrity: MCP allows you to communicate how your data should be interpreted, reducing the risk of AI systems drawing incorrect conclusions from your visualizations.

Implementing MCP for Your Health Equity Data Platform

Here's how you could implement MCP for your health equity data platform:

1. Basic MCP Implementation

First, you'd need to add a simple JSON-LD script tag to your website's pages. This script would contain structured data about your content:

<script type="application/ld+json">
{
  "@context": "https://mcp.anthropic.com/v1/context.json",
  "@type": "WebPage",
  "name": "Health Equity Data - Racial Disparities in Maternal Health",
  "description": "Data visualization and analysis of maternal health outcomes across racial demographics in the United States.",
  "url": "https://healthequitydata.org/reports/maternal-health-2024",
  "datePublished": "2024-09-15"
}
</script>

2. Enhancing Data Visualizations with MCP

For your data visualizations, you can add specific MCP markup to help AI systems understand what they represent:

<script type="application/ld+json">
{
  "@context": "https://mcp.anthropic.com/v1/context.json",
  "@type": "Dataset",
  "name": "Maternal Mortality by Race and Ethnicity (2015-2023)",
  "description": "Comparison of maternal mortality rates across racial and ethnic groups in the US",
  "contentUrl": "https://healthequitydata.org/data/maternal-mortality-by-race.json",
  "variableMeasured": [
    {
      "@type": "PropertyValue",
      "name": "maternalMortalityRate",
      "description": "Maternal deaths per 100,000 live births",
      "unitText": "deaths per 100,000 live births"
    }
  ],
  "datasetTimeInterval": "2015-01/2023-12",
  "spatialCoverage": {
    "@type": "Place",
    "name": "United States"
  }
}
</script>

3. Implementing Methodology and Context

To ensure AI systems understand how to interpret your data:

<script type="application/ld+json">
{
  "@context": "https://mcp.anthropic.com/v1/context.json",
  "@type": "HealthData",
  "name": "Health Equity Analysis Methodology",
  "description": "Our approach to analyzing health disparities accounts for social determinants of health, access factors, and demographic variables.",
  "methodology": {
    "dataCollection": "CDC WONDER database, BRFSS surveys, and hospital discharge data",
    "statisticalMethods": "Age-adjusted rates with 95% confidence intervals",
    "limitations": "Data may have reporting gaps in certain regions and demographic groups"
  },
  "contextualFactors": [
    "Social determinants of health",
    "Geographic access to care",
    "Insurance coverage",
    "Provider bias"
  ]
}
</script>

4. Providing Machine-Readable Reports

For your comprehensive reports:

<script type="application/ld+json">
{
  "@context": "https://mcp.anthropic.com/v1/context.json",
  "@type": "Report",
  "name": "Racial Disparities in Diabetes Care and Outcomes",
  "headline": "Black and Hispanic Americans face 2-3x higher rates of diabetes complications despite similar disease prevalence",
  "author": {
    "@type": "Organization",
    "name": "Health Equity Data Initiative"
  },
  "datePublished": "2024-08-10",
  "keyFindings": [
    {
      "@type": "Finding",
      "name": "Amputation disparity",
      "description": "Black patients are 3x more likely to undergo lower-extremity amputations due to diabetes complications",
      "confidenceLevel": "High",
      "basedOn": "Analysis of Medicare claims data 2018-2023"
    },
    {
      "@type": "Finding",
      "name": "Geographic variation",
      "description": "Racial disparities are most pronounced in the Southeast and in areas with provider shortages",
      "confidenceLevel": "Medium",
      "basedOn": "County-level analysis of healthcare access metrics"
    }
  ]
}
</script>

Implementation Approach

1. Start with key pages: Begin by implementing MCP on your most important reports and dashboards.

2. Automate where possible: Integrate MCP generation into your existing data pipeline so the JSON-LD is automatically generated when reports are published.

3. Test with AI systems: You can test your implementation by seeing how AI assistants interpret your content before and after MCP implementation.

4. Provide an API: Consider also offering a dedicated MCP API endpoint that delivers the same structured data for direct machine consumption.

MCP API Implementation for Health Equity Data

An MCP API for your health equity data platform would provide a structured way for AI systems to directly access your valuable health disparity information. Here's what it would look like:

Core API Structure

Your MCP API would have these key components:

1. Base Endpoint

https://healthequitydata.org/api/mcp/v1/

2. Resource Endpoints

/datasets            # List available datasets
/datasets/{id}       # Specific dataset with MCP metadata
/reports             # List available reports
/reports/{id}        # Specific report with MCP metadata
/visualizations      # List available visualizations
/visualizations/{id} # Specific visualization with MCP metadata

3. Query Parameters

?format=json         # Default response format
?detailed=true       # Include full dataset attributes
?geo=state           # Filter by geographic level
?demographic=race    # Filter by demographic dimension
?condition=diabetes  # Filter by health condition

Example API Response

Here's what a typical response from your MCP API might look like:

{
  "@context": "https://mcp.anthropic.com/v1/context.json",
  "@type": "HealthEquityDataset",
  "id": "maternal-mortality-2023",
  "name": "Maternal Mortality by Race and Ethnicity (2023)",
  "description": "Age-adjusted maternal mortality rates across racial and ethnic groups in the US",
  "url": "https://healthequitydata.org/datasets/maternal-mortality-2023",
  "datePublished": "2024-02-15",
  "temporalCoverage": "2023-01-01/2023-12-31",
  "spatialCoverage": {
    "@type": "Place",
    "name": "United States"
  },
  "publisher": {
    "@type": "Organization",
    "name": "Health Equity Data Initiative",
    "url": "https://healthequitydata.org"
  },
  "license": "CC-BY-4.0",
  "datasetSchema": {
    "dimensions": [
      {
        "name": "race_ethnicity",
        "categories": ["White", "Black", "Hispanic", "Asian/PI", "AI/AN", "Multiple Races"]
      },
      {
        "name": "age_group",
        "categories": ["15-19", "20-24", "25-29", "30-34", "35-39", "40+"]
      },
      {
        "name": "geography",
        "categories": ["National", "State", "County"]
      }
    ],
    "measures": [
      {
        "name": "mortality_rate",
        "description": "Deaths per 100,000 live births",
        "unitText": "deaths per 100,000 live births",
        "statisticalMethod": "Age-adjusted rate with 95% CI"
      },
      {
        "name": "disparity_ratio",
        "description": "Ratio compared to reference group (White)",
        "unitText": "ratio"
      }
    ]
  },
  "dataValues": [
    {
      "race_ethnicity": "White",
      "mortality_rate": 19.1,
      "confidence_interval": [17.8, 20.4],
      "sample_size": 1874123
    },
    {
      "race_ethnicity": "Black",
      "mortality_rate": 55.3,
      "confidence_interval": [51.2, 59.4],
      "sample_size": 583921,
      "disparity_ratio": 2.89
    },
    // Additional data values...
  ],
  "methodology": {
    "dataSource": ["CDC WONDER Database", "State vital statistics"],
    "inclusionCriteria": "All maternal deaths within 42 days of pregnancy termination",
    "adjustmentFactors": ["Age", "Education", "Insurance status"],
    "limitations": "Potential underreporting in certain jurisdictions"
  },
  "contextualFactors": [
    "Access to prenatal care",
    "Provider availability in underserved communities",
    "Structural racism in healthcare systems",
    "Social determinants of health"
  ],
  "citations": [
    {
      "@type": "ScholarlyArticle",
      "name": "Persistent Racial Disparities in Maternal Health Outcomes",
      "author": "Johnson et al.",
      "datePublished": "2023",
      "url": "https://doi.org/10.xxxx/xxxxx"
    }
  ]
}

API Documentation

You would also provide comprehensive documentation:

1. Authentication: How AI systems can authenticate (likely API keys)
2. Rate limiting: Constraints on requests (e.g., 100 requests per minute)
3. Schema definitions: Detailed explanation of all data types and fields
4. Example queries: Common usage patterns for AI systems
5. Change history: Documentation of API versioning and changes

Integration Points

Your MCP API could also include:

1. Webhooks: Notification when new reports are published
2. Bulk data access: For more comprehensive analysis
3. Interactive query builder: For testing queries before implementation

This API structure would make your health equity data easily consumable by AI systems while maintaining the contextual nuance and methodological details necessary for proper interpretation of health disparity information.