# FRED Series MCP

> FRED Series — U.S. Economic Time Series accesses over 816,000 official economic indicators from the Federal Reserve. You can pull raw data points for anything from GDP and inflation to interest rates and unemployment figures. It handles complex requests like year-over-year percentage changes or aggregating daily readings into quarterly summaries automatically.

## Overview
- **Category:** the-unthinkable
- **Price:** Free
- **Tags:** time-series, economic-indicators, data-transformation, macroeconomics, financial-analysis, statistical-data

## Description

This MCP connects your AI agent directly to the full FRED database, giving you access to America's most comprehensive economic data engine. You don't have to manually sift through spreadsheets looking for CPI numbers or federal funds rates; you just ask for what you need. Your client handles searching across hundreds of thousands of indicators by keyword. Need historical context? It pulls in ALFRED-style revisions so you can see how the data was adjusted over time. Furthermore, it doesn't just give you a raw number; it performs built-in transformations, like calculating year-over-year percentage changes or aggregating daily metrics up to quarterly totals, all with one prompt. Connecting this MCP via Vinkius gives your agent access to the entire catalog of tools needed for deep financial and economic analysis, making complex research instantaneous.

## Tools

### search_series
Searches through all 816,000+ economic indicators by keyword to find the correct series ID or metadata.

### get_series
Retrieves core details, including units and frequency, for a specific known FRED series using its official ID.

### get_observations
Pulls the actual data values for a time series, allowing you to filter by date range or request built-in transformations.

### get_series_updates
Checks which economic indicators were recently updated, helping track the release of new official data points.

### get_vintage_dates
Provides historical revision dates for a series, crucial for understanding how past data was officially adjusted.

## Prompt Examples

**Prompt:** 
```
What is the current U.S. unemployment rate?
```

**Response:** 
```
📊 **U.S. Unemployment Rate (UNRATE)**

Latest: 3.7% (January 2025)
Previous: 3.7% (December 2024)
Frequency: Monthly, seasonally adjusted

12-month trend: Stable around 3.5-3.9%
Historical low: 2.5% (May 1953)
Pandemic peak: 14.7% (April 2020)

The labor market remains tight by historical standards.
```

**Prompt:** 
```
Show me U.S. GDP growth rate over the last 5 years
```

**Response:** 
```
📈 **U.S. GDP — Percent Change from Previous Quarter (Annualized)**

| Year | Q1 | Q2 | Q3 | Q4 |
|------|-----|-----|-----|-----|
| 2024 | 1.4% | 3.0% | 2.8% | 2.3% |
| 2023 | 2.0% | 2.1% | 4.9% | 3.4% |
| 2022 | -1.6% | -0.6% | 3.2% | 2.6% |

Using series GDP with units=pch (percent change) and frequency=q (quarterly).
```

**Prompt:** 
```
Compare the federal funds rate with 10-year Treasury yield
```

**Response:** 
```
🏦 **FEDFUNDS vs DGS10 — Rate Comparison**

Federal Funds Rate (FEDFUNDS): 5.33%
10-Year Treasury (DGS10): 4.15%

Yield curve inversion: -118 bps
This inversion has persisted for 18+ months, historically signaling recession risk.

Both series retrieved from FRED with daily frequency.
```

## Capabilities

### Find any U.S. economic indicator
Your agent searches a massive database by keyword to locate indicators like unemployment rates or housing starts.

### Pull raw date/value pairs
It retrieves actual data points for specific series, supporting complex filters and time ranges.

### Transform and aggregate metrics
The MCP automatically calculates units like percent change or aggregates daily data into monthly or annual totals.

### Analyze historical revisions
You can access vintage analysis to understand how official data has been revised by the Federal Reserve over decades.

## Use Cases

### Determining investment risk based on yield curve shifts
A financial advisor needs to compare the federal funds rate against 10-year treasury yields. They ask their agent to use `get_observations` to pull both series, allowing them to instantly calculate and track the yield curve inversion over time for client reports.

### Building a quarterly economic forecast model
A quantitative analyst requires historical GDP data. Instead of pulling daily records, they use `get_observations` and specify aggregation from daily to quarterly, getting clean, ready-to-use numbers for their forecasting script.

### Validating a thesis on long-term inflation trends
An academic researcher must account for historical data revisions. They use `get_vintage_dates` to retrieve the official revision history of the CPI series, ensuring their findings are based on the most accurate, adjusted metrics.

### Monitoring market reaction to new Fed announcements
A portfolio manager wants to know if any key interest rate indicators were updated that morning. They run `get_series_updates` to quickly identify recently changed data before calling their trading team.

## Benefits

- Stop guessing which indicator ID you need. Use `search_series` to find any of the 816,000+ indicators—from GDP to CPI—just by typing a keyword like 'unemployment rate'.
- You never have to manually calculate percentage changes or aggregate data again. The `get_observations` tool handles unit transformations and frequency adjustments automatically.
- Need to know if the current inflation number is accurate? Use `get_vintage_dates` for ALFRED-style analysis, seeing exactly how the Federal Reserve revised past metrics.
- Get a full profile of any indicator using `get_series`. This gives you all the metadata—units, source, frequency—before you even pull the data points.
- Track major economic announcements without constant manual checks. The `get_series_updates` tool alerts your agent when key macro series have been newly released.

## How It Works

The bottom line is you get instant access to authoritative economic data without needing specialized database knowledge.

1. First, you tell your agent what economic indicator you need and which time period you're interested in.
2. Next, your client runs the request through this MCP, letting it handle the data retrieval, applying necessary unit transformations (like calculating percent change), and aggregating the results into a usable format.
3. You get back clean, structured data ready for analysis—whether that's a comparison of interest rates or a multi-year trend chart.

## Frequently Asked Questions

**How do I use FRED Series MCP to find a series ID?**
Use `search_series`. You just need to type keywords like 'inflation' or 'unemployment rate', and the tool returns matching indicators with their full metadata.

**Can I calculate annual percentage change using get_observations?**
Yes. When calling `get_observations`, you specify units=pch (percent change) in your request. The MCP performs that calculation for you on the raw data points.

**Is FRED Series MCP suitable for modeling future rates?**
No, this MCP only retrieves historical and current official data from the Federal Reserve. It is a data source, not a predictive model; it provides facts, not forecasts.

**What if I need to compare multiple indicators like CPI and GDP?**
You can retrieve multiple series in one go using `get_observations`. Just provide the list of desired indicator IDs, and your agent will pull all the corresponding data points for comparison.

**How do I check if FRED Series MCP has new data releases?**
Run the `get_series_updates` tool. This checks which major economic indicators were recently updated, which is key for tracking breaking macro news.