FRED Full Access MCP for AI Agents. Run complex U.S. economic queries in seconds.
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.
Give Claude and any AI agent real-world access
You can search for any specific economic time series using keywords to find matching metadata, popularity scores, and units.
The tool gathers cross-sectional economic data, allowing you to compare metrics like unemployment rates across different states or Metropolitan Statistical Areas (MSAs).
You can list all scheduled and past economic data releases, helping your agent build a full economic calendar.
The MCP navigates the FRED category tree, letting you filter for series that belong to specific domains like National Accounts or Prices.
You pull more than just the latest number. You get vintage dates and detailed metadata, which is critical when doing deep academic analysis.
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What AI agents can do with FRED Full Access — U.S. Economic Intelligence (21 Tools)
Use these tools to search, retrieve, categorize, and analyze massive amounts of official U.S. economic time series data from the Federal Reserve.
Make your AI actually useful.
Add this MCP to Claude, Cursor, or Windsurf and your AI stops guessing. It gets real tools to look things up, take action, and handle the stuff you keep doing by hand.
Start using FRED Full Access — U.S. Economic Intelligence MCPSearch 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...
Get Category Series
Pulls a list of actual data series that fall within a specific, defined FRED...
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...
Search Tags
Allows you to search or browse the entire library of tags available across all FRED...
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...
Get Vintage Dates
Provides historical revision dates for a series, essential for accurate vintage...
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.
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Build a custom MCP for your own tools, or connect a ready-made integration from our catalog.
Build Your Own
Turn any API into an MCP. Import a spec, define Agent Skills, or deploy with MCPFusion.
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Make Your AI Do More
Start with FRED Full Access — U.S. Economic Intelligence, then connect any of our 5,200+ other servers whenever your AI needs more. One click, no limits.
- Use this MCP plus 5,200+ others, all in one place
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The headache of manual data aggregation Solved with Vinkius AI Gateway
Right now, analyzing U.S. economic trends requires jumping between dozens of websites and APIs. You pull GDP from one source, unemployment from another, state-level inflation from a third, and then you have to manually figure out if the numbers are seasonally adjusted or vintage revisions. It's an archaeological dig just to build one chart.
With this MCP, your agent handles all that complexity behind the scenes. You tell it: 'Give me the CPI for California last quarter.' And it figures out which specific series ID and geographic tool you need—it’s like having every data expert on staff.
Getting Cross-Sectional Data with get_regional_data
Before, comparing a state's job market to the national average was a multi-step mess. You had to find the general series ID for 'unemployment,' then separately pull data for every single county you cared about, and finally stitch it all together in Excel. It’s tedious.
Now, calling get_regional_data lets your agent handle the entire cross-section. You define the metric, and it returns a unified dataset comparing that metric across states, counties, or MSAs instantly.
What your AI can actually do with this
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.
019d759e-f971-71db-ab13-1b60c32d03d7 Here's how it actually 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.
First, your AI client uses a search tool to narrow down the 816,000+ available series by name, category, or tag.
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.
Finally, your agent receives a structured dataset containing the required time series, metadata, and historical context from the Federal Reserve.
Who is this actually for?
Anyone who writes reports about the economy, from academic researchers and financial analysts to policy advisors. This MCP saves the day for professionals tired of manually querying dozens of separate data endpoints just to build one coherent view.
They use this tool to pull multiple indicators (like CPI, GDP, and unemployment) simultaneously to run comparative models and generate briefing materials.
They query specific series like the 10-year Treasury yield or M2 money supply, then use regional data to compare state market performance against national averages.
They rely on the category and tag tools to discover all relevant data points for a niche policy area (e.g., clean energy spending) across different regions.
What Changes When You Connect
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.
See it in action
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.
The honest tradeoffs
What to watch out for, and the recommended way to handle each one.
Treating data like a simple database lookup
Asking for 'the unemployment rate' without specifying if you want national, state-level, or seasonally-adjusted figures. This leads to ambiguous and incomplete reports.
Always refine your query. Use get_series first to confirm the exact series ID (e.g., UNRATE) and then specify which geographic scope or adjustment is necessary when calling get_observations.
Ignoring data structure complexity
Trying to find all related datasets for 'housing' by just keyword searching, resulting in thousands of irrelevant hits from unrelated economic topics.
First, use the category tools (get_category -> get_category_children) to navigate the official taxonomy tree. Then, use get_series_by_tags to filter only relevant series.
Assuming all data is current
Building a report that uses today's numbers without checking if a major release (like GDP) was scheduled or revised yesterday. The final analysis will be factually wrong.
Always check the economic calendar first using list_releases and get_release_dates to confirm what data is due, and then use get_series_updates to see if any key metrics were recently changed.
When It Fits, When It Doesn't
Use this MCP if your project requires deep, authoritative U.S. economic context—think macroeconomics, financial modeling, or policy analysis. You need the depth of data provided by the Federal Reserve; it’s not just a simple stock ticker lookup. Don't use this if you only need general market sentiment or data from niche sectors outside government reporting (like private credit card spending). If your goal is simply to compare two unrelated company revenues, look for a general financial metrics MCP instead. However, if you are comparing the performance of state economies against national averages, this is the definitive tool set.
Questions you might have
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.