# FRED GeoFRED Regional Data MCP

> FRED GeoFRED — Regional Economic Data connects your AI client to comprehensive U.S. economic metrics. It provides unemployment, income levels, and GDP data broken down by state, county, MSA, or Federal Reserve District. Get cross-sectional regional comparisons and necessary geographic boundaries for detailed analysis.

## Overview
- **Category:** brain-trust
- **Price:** Free
- **Tags:** regional-economics, geographic-data, demographics, economic-indicators, mapping, spatial-analysis

## Description

If you're building a dashboard that needs to show how economics play out across the country, this MCP is what you need. It takes standard national economic time series data—like unemployment rates or median income—and breaks it down into specific geographical regions. You can pull regional snapshots for any U.S. area type, including states, counties, and metro areas. Plus, it gets you the GeoJSON-compatible shape files required to actually map that data visually. When your agent needs to compare performance across different geographic boundaries or build a spatial analysis piece, this MCP makes that possible. You'll connect through Vinkius, giving your AI client access to thousands of other specialized tools alongside these economic datasets.

## Tools

### get_regional_data
Retrieves cross-sectional regional economic data for specified U.S. areas like states or counties.

### get_series_group
Determines the available region types and units for any given FRED series ID to guide your analysis.

### get_geo_shapes
Downloads standardized geographic shape files, including boundaries for counties and MSAs, ready for mapping.

## Prompt Examples

**Prompt:** 
```
What is the unemployment rate by state?
```

**Response:** 
```
📊 **Unemployment Rate by State (Latest)**

| State | Rate |
|-------|------|
| Nevada | 5.4% |
| DC | 5.2% |
| California | 5.1% |
| ...national avg... | 3.7% |
| South Dakota | 2.0% |
| Vermont | 1.9% |
| North Dakota | 1.8% |

51 regions returned from GeoFRED.
```

**Prompt:** 
```
Compare median household income across metro areas
```

**Response:** 
```
💵 **Median Household Income — Top 5 MSAs**

1. San Jose-Sunnyvale: $145K
2. Washington-Arlington: $118K
3. San Francisco-Oakland: $115K
4. Seattle-Tacoma: $105K
5. Boston-Cambridge: $102K

National median: $75K
```

**Prompt:** 
```
Get the geographic boundaries for U.S. states
```

**Response:** 
```
🗺 **State Boundaries — GeoJSON**

Returned shape data for all 50 states + DC.
Includes: FIPS codes, state names, boundary coordinates.

Use with regional data to build choropleth maps.
```

## Capabilities

### Map Geographic Boundaries
Retrieve the GeoJSON shape files needed for mapping data across specific U.S. regions.

### Discover Data Availability
Check which types of geographic breakdowns (like state or county) exist for a given economic metric.

### Get Cross-Sectional Regional Metrics
Fetch specific regional economic data points, such as unemployment rates or income levels, broken down by geography.

## Use Cases

### A Housing Market Analysis
A real estate consultant needs to compare median income across three specific metro areas (MSAs) and see which one has the lowest housing price index. The agent first uses get_series_group to validate the data, then calls get_regional_data for the metrics, and finally asks for get_geo_shapes to create a visual comparison map.

### Policy Briefing on Unemployment
A policy advisor needs to show the difference in unemployment rates between states versus counties. The agent uses get_series_group to confirm both region types are available, then runs get_regional_data twice—once for the state level and once for the county level—to create a comprehensive report.

### Academic Research on Poverty
A researcher needs standardized boundaries for mapping poverty rates across all 50 states. The agent first uses get_series_group to validate the 'poverty' series, then calls get_geo_shapes to get the state outlines, and finally runs get_regional_data to populate the metrics.

### Inter-State Business Comparison
A corporate strategy team wants to compare GDP growth across different Federal Reserve Districts (FRB). The agent uses get_series_group to confirm FRB is a valid region type, then calls get_regional_data for the specific time period and district grouping.

## Benefits

- See regional snapshots of key metrics. You can pull specific data like unemployment, income levels, or poverty rates for any state, county, or MSA using get_regional_data.
- Validate your data scope instantly. Before pulling figures, use get_series_group to confirm exactly what geographic breakdowns exist for a given economic indicator and its units.
- Build professional maps quickly. The MCP provides necessary GeoJSON-compatible shape files via get_geo_shapes, allowing you to visualize regional boundaries alongside the pulled metrics.
- Compare performance across diverse areas. This tool lets you compare regions from various groupings (like BEA or FRB) without switching data sources or spreadsheets.
- Avoid data gaps in your reports. By checking available region types through get_series_group, you ensure your agent doesn't miss necessary geographic scope when running analyses.

## How It Works

The bottom line is that you guide your AI client through three specific calls: discovery of available regions, retrieval of regional metrics, and fetching the map boundaries.

1. First, use the get_series_group tool with a FRED series ID (like UNRATE) to figure out what geographic breakdowns and data units are available.
2. Next, call get_regional_data. This sends the specific metric, the desired region type (e.g., county), and any necessary filters for your agent to pull the actual cross-sectional numbers.
3. Finally, if you need to plot this data visually, use get_geo_shapes to retrieve the corresponding boundary files needed for mapping.

## Frequently Asked Questions

**How do I know if a metric is available by county using get_series_group?**
You run get_series_group with the desired FRED series ID. The resulting metadata will list all supported region types, confirming whether 'county' or other local breakdowns are valid for that specific metric.

**What if I need to compare data across different regions like BEA and FRB?**
You can use get_regional_data multiple times. You just need to confirm the region type (bea or frb) is valid for your metric first, using get_series_group.

**Does FRED GeoFRED support international data?**
No, this MCP is specialized for U.S. internal economics. It supports US region types like state, county, and MSA, but not global country comparisons.

**Can I get the boundaries for multiple regions at once with get_geo_shapes?**
Yes, you specify the desired shape type (e.g., 'state' and 'county') in a single call to get_geo_shapes, ensuring consistency across your mapping project.

**What kind of data can I pull using get_regional_data?**
You can retrieve various cross-sectional economic metrics including unemployment, median income, poverty rates, and GDP breakdowns for specific US regions.