# FRED Full Access MCP

> FRED Full Access delivers massive U.S. economic intelligence directly to your AI client. This MCP provides access to over 816,000 time series from the Federal Reserve. It handles everything from quarterly GDP reports and unemployment rates to regional data for every state and county across the US, all in one place.

## Overview
- **Category:** the-unthinkable
- **Price:** Free
- **Tags:** economic-intelligence, time-series, macroeconomics, financial-data, data-retrieval, federal-reserve

## Description

Trying to track macro trends usually means juggling five different data feeds: CPI, employment, interest rates, state-level metrics, and historical revisions. This MCP pulls it all together. You don't have to install separate integrations for each dataset or manually cross-reference 107 different official data sources. Instead, you let your AI client query the entire FRED taxonomy tree directly.

Whether you need to find a series by its category, look at historical revisions (vintage analysis), or see how unemployment rates change across states and counties, this connector gives you the necessary tools. It allows your agent to manage complex data discovery—like finding all housing-related tags that are quarterly and seasonally adjusted—and pull out the raw numbers for any date range. You'll find connecting this massive dataset in the Vinkius catalog makes running multi-domain economic analysis straightforward.

## Tools

### search_series
Looks up matching economic series by keyword, returning key details like frequency and unit.

### get_category
Retrieves information about a specific FRED data category using its unique ID.

### get_category_children
Explores the taxonomy tree by listing the child categories of any given parent category ID.

### get_category_series
Pulls a list of actual data series that fall within a specific, defined FRED category.

### get_category_tags
Shows all relevant tags associated with a particular economic category ID.

### get_regional_data
Retrieves cross-section data, allowing you to compare metrics across different US geographical regions (states, counties).

### get_series_group
Determines the group ID needed for regional data analysis by using an existing FRED series ID.

### get_geo_shapes
Downloads geographic shape files, which are necessary if you need to map out economic regions in a visual format.

### search_tags
Allows you to search or browse the entire library of tags available across all FRED data.

### get_series_by_tags
Finds and returns series that specifically match one or more input keywords (tags).

### list_sources
Lists every official data provider source contributing to the FRED database.

### get_series
Fetches detailed metadata, including units and frequency, for any specific known FRED series ID (like GDP or UNRATE).

### get_observations
Pulls the actual recorded data values for a time series, supporting date filtering and unit transformations.

### get_series_updates
Identifies which FRED series have been recently updated or revised, helping you track changes in macro data.

### get_vintage_dates
Provides historical revision dates for a series, essential for accurate vintage analysis (ALFRED-style).

### list_releases
Lists all major economic data release events published by the Federal Reserve.

### get_release
Gets detailed information about a specific, named economic report or release event.

### get_release_dates
Fetches the calendar dates for major economic data releases, useful for building an economic timeline.

### get_release_series
Lists all individual series that were published within a specific named release event.

## Prompt Examples

**Prompt:** 
```
Complete macro briefing: GDP, unemployment, inflation, and Fed rate
```

**Response:** 
```
🇺🇸 **U.S. Macro Dashboard**

| Indicator | Value | Trend |
|-----------|-------|-------|
| GDP Growth | +2.3% | → |
| Unemployment | 3.7% | → |
| CPI Inflation | 3.1% | ↓ |
| Fed Funds Rate | 5.33% | → |
| 10Y Treasury | 4.15% | ↓ |
| S&P 500 | 5,950 | ↑ |

6 series queried simultaneously from FRED Full.
```

**Prompt:** 
```
Which state has the lowest unemployment and what are the upcoming economic releases?
```

**Response:** 
```
📊 **Cross-Domain Query — GeoFRED + Releases**

🥇 Lowest unemployment: North Dakota (1.8%)
🥈 Vermont (1.9%)
🥉 South Dakota (2.0%)

📅 Next releases:
- Jan 31: GDP Advance (BEA)
- Feb 3: ISM Manufacturing
- Feb 7: Employment Situation (BLS)

2 tools used: get_regional_data + get_release_dates
```

**Prompt:** 
```
Find all quarterly seasonally-adjusted series related to housing
```

**Response:** 
```
🏠 **Housing Series (quarterly + SA)**

Found via tags: housing + quarterly + sa

1. USSTHPI — House Price Index
2. HOUST — Housing Starts
3. MORTGAGE30US — 30-Year Mortgage Rate
4. MSPUS — Median Sales Price
5. RHORUSQ156N — Homeownership Rate

+45 more matching series
```

## Capabilities

### Search across 816,000+ data points
You can search for any specific economic time series using keywords to find matching metadata, popularity scores, and units.

### Analyze regional trends by state or county
The tool gathers cross-sectional economic data, allowing you to compare metrics like unemployment rates across different states or Metropolitan Statistical Areas (MSAs).

### Track official data releases and dates
You can list all scheduled and past economic data releases, helping your agent build a full economic calendar.

### Build structured datasets by taxonomy
The MCP navigates the FRED category tree, letting you filter for series that belong to specific domains like National Accounts or Prices.

### Retrieve historical revisions and metadata
You pull more than just the latest number. You get vintage dates and detailed metadata, which is critical when doing deep academic analysis.

## Use Cases

### Comparing state performance post-pandemic
A policy researcher needs to know which states recovered fastest. They use get_series_group and then get_regional_data, comparing unemployment rates across every county in the US for a specific time period.

### Building an automated quarterly briefing
A financial analyst needs to compile GDP, inflation (CPIAUCSL), and employment data. They use get_observations multiple times within one workflow, ensuring they pull the correct units and date filters for a cohesive report.

### Finding all housing-related metrics
A developer wants to build a dashboard on US home prices but doesn't know the exact series IDs. They use get_series_by_tags with 'housing' and then filter by frequency to find only quarterly data.

### Forecasting using historical revisions
A macroeconomist is skeptical of current inflation figures, so they use get_vintage_dates on the CPI series to prove how often and when the numbers have been retroactively adjusted by the Fed.

## Benefits

- You stop managing multiple data integrations and connect once to access over 816,000 time series from the Federal Reserve. This is a massive consolidation of sources.
- Instead of manually cross-referencing state reports, use get_regional_data to pull comparable metrics across all US states or counties simultaneously.
- Track data changes accurately by utilizing get_vintage_dates; this shows when historical numbers were revised, which standard APIs often omit.
- You build a full economic calendar using list_releases and get_release_dates, so your agent can alert you to upcoming reporting dates before the data even drops.
- Discovery is streamlined: Use search_series or get_series_by_tags instead of browsing endless menus to pinpoint exactly what dataset you need.

## How It Works

The bottom line is you get instant access to decades of highly granular economic data without needing multiple API keys or complex manual database queries.

1. First, your AI client uses a search tool to narrow down the 816,000+ available series by name, category, or tag.
2. Next, you instruct it to pull specific data—for example, getting the observed values for GDP over the last five years, making sure to specify date ranges and desired unit transformations.
3. Finally, your agent receives a structured dataset containing the required time series, metadata, and historical context from the Federal Reserve.

## Frequently Asked Questions

**How do I find all related data for housing using FRED Full Access MCP?**
You should start by using get_series_by_tags. Search tags like 'housing,' then use the results to filter and gather all relevant series, rather than guessing individual IDs.

**Can I compare multiple states' unemployment rates with FRED Full Access MCP?**
Yes, you use get_regional_data. You just need to identify the correct series group ID using get_series_group first to ensure your comparison is accurate across all regions.

**Does FRED Full Access MCP handle historical data revisions?**
Absolutely. The tool provides get_vintage_dates, which is essential for advanced analysis because it tells you exactly when a historical number was revised or updated by the Fed.

**What is the best way to start searching for economic data using FRED Full Access MCP?**
Start with search_series. It lets your agent quickly match keywords against 816,000+ series, giving you an overview of what's available before diving into specific details.

**Do I need to know the exact data ID for every query?**
No. You can use get_category and its child tools (get_category_children) to navigate the entire FRED taxonomy tree, letting your agent find the correct IDs based on topic.