PopHIVE MCP Server
Server Configuration
Describes the environment variables required to run the server.
| Name | Required | Description | Default |
|---|---|---|---|
| NODE_ENV | No | Environment mode | development |
| DATA_CACHE_DIR | No | Directory for cached data files | ./data |
| UPDATE_FREQUENCY | No | Data refresh frequency (hourly, daily, weekly) | daily |
Instructions
Guidance the server publishes about itself, which clients place ahead of the tool catalog so the model reads it before choosing anything.
This server publishes no instructions, or was last inspected before Glama recorded them.
Capabilities
Features and capabilities supported by this server
Protocol revision2025-11-25
| Capability | Details |
|---|---|
| tools | {} |
| prompts | {} |
| resources | {} |
Tools
Functions exposed to the LLM to take actions
| Name | Description |
|---|---|
| filter_dataB | Filter PopHIVE datasets by various criteria (state, date range, demographics) |
| compare_statesC | Compare health metrics across multiple states |
| time_series_analysisC | Analyze trends over time for specific health metrics |
| get_available_datasetsB | Get comprehensive information about all available PopHIVE datasets |
| search_health_dataB | Search across all datasets for specific health conditions, metrics, or keywords |
Prompts
Interactive templates invoked by user choice
| Name | Description |
|---|---|
| immunization_gaps | Analyze vaccination coverage gaps by demographic factors |
| respiratory_surge_detection | Identify and analyze respiratory disease surges |
| chronic_disease_trends | Analyze chronic disease prevalence trends and patterns |
| multi_source_analysis | Comprehensive analysis using multiple data sources |
Resources
Contextual data attached and managed by the client
| Name | Description |
|---|---|
| CDC National Immunization Survey Data | Household survey data on childhood vaccination coverage rates |
| Epic Cosmos Immunization Data | EHR-based immunization data by insurance status and urbanicity |
| Emergency Department Visits | ED visits for respiratory viruses (RSV, COVID-19, Influenza) |
| Laboratory Test Positivity | Lab test positivity rates from CDC NREVSS |
| Wastewater Surveillance | Viral levels in wastewater from CDC NWWS |
| Google Health Trends | Search trends for respiratory symptoms |
| Obesity Prevalence | BMI ≥30 prevalence by state and age group |
| Diabetes Prevalence | HbA1c ≥7% prevalence by state and age group |
| HHS Hospital Capacity | State-level data on hospital bed capacity and utilization |
| CDC Injury, Overdose, and Violence Data | National-level data on injury, overdose, and violence-related deaths |
| CDC Youth Mental Health ED Visits | National-level data on mental health-related emergency department visits for youth |
TDQS
Scored across 5 tools
Each tool serves a clearly distinct purpose: comparing states, filtering datasets, listing datasets, searching, and time series analysis. No two tools have overlapping functionality.
Most tools follow a verb_noun pattern (compare_states, filter_data, get_available_datasets, search_health_data). time_series_analysis is slightly less imperative but still descriptive and consistent in style.
With 5 tools, the server is well-scoped for a health data exploration API. Each tool addresses a key operation without redundancy or bloat.
The tool set covers core data exploration needs: discovery, search, filtering, comparison, and trend analysis. Minor missing features like data export exist but are optional for a query-focused server.