FRED Full Access MCP. Access 816,000+ U.S. economic time series in one place.
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FRED Full Access — U.S. Economic Intelligence is a Mega-Server that gives your AI client direct access to over 816,000 time series from the Federal Reserve.
It handles everything from retrieving specific economic data points (like GDP or unemployment rates) to exploring data by regional boundaries, category taxonomy, or tags.
You don't need to manage multiple data integrations; this one server covers the full scope of U.S. macroeconomics.
What your AI agents can do
Get category
Retrieves a FRED category using a specific ID (e.g., Money/Banking, Employment).
Get category children
Finds the child categories under a given parent category ID.
Get category series
Lists all available time series that fall within a specific category, allowing filtering by units or frequency.
Gets actual data values for a specific FRED time series, supporting date filtering and unit transformations.
Searches through over 816,000 economic time series using keywords, popularity, or specific tags.
Retrieves cross-sectional economic data for defined geographic areas like states, counties, or MSAs.
Navigates the FRED category tree to discover and filter data by topic, starting from broad categories.
Lists upcoming and past economic data releases, allowing you to find all series published in a specific report.
Finds metadata on series, including recent updates or historical revision dates (vintage analysis).
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FRED Full Access: 19 Tools for Economic Data Retrieval
These tools allow your AI client to search, map, and pull every type of economic data from the Federal Reserve, eliminating the need for multiple data sources.
019d759eget category
Retrieves a FRED category using a specific ID (e.g., Money/Banking, Employment).
019d759eget category children
Finds the child categories under a given parent category ID.
019d759eget category series
Lists all available time series that fall within a specific category, allowing filtering by units or frequency.
019d759eget category tags
Retrieves all tags associated with a given data category.
019d759eget geo shapes
Gets geographic shape files (like BEA, MSA, or state boundaries) needed for mapping regional data.
019d759eget observations
Pulls the actual time series data values for a specified FRED series ID, with options for date filtering and unit adjustments.
019d759eget regional data
Retrieves cross-sectional economic data for specific regional groups, using a previously found series group ID.
019d759eget release
Gets detailed information about a single, specific economic data release (e.g., the CPI report).
019d759eget release dates
Lists upcoming and past dates for major economic data releases, providing a full economic calendar.
019d759eget release series
Retrieves every single series that was published within a specified economic data release.
019d759eget series
Retrieves metadata and details for a single FRED series ID (e.g., GDP, UNRATE).
019d759eget series by tags
Finds all series that match specific, user-defined tags (e.g., 'usa;gdp').
019d759eget series group
Determines the group ID for a series, which is needed to pull cross-sectional regional data.
019d759eget series updates
Lists all FRED series that have been recently updated, allowing you to track data maintenance.
019d759eget vintage dates
Gets the historical revision dates for a series, which is critical for vintage analysis.
019d759elist releases
Lists all available economic data releases tracked by FRED.
019d759elist sources
Retrieves a list of all official data sources that contribute data to FRED.
019d759esearch series
Searches over 816,000 economic time series using keywords, returning title, frequency, and popularity.
019d759esearch tags
Searches or browses the full library of FRED tags to discover series by subject matter.
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What you can do with this MCP connector
Hey, this server gives your AI client direct access to the Federal Reserve's entire economic data catalog—over 816,000 time series in one place. You won't gotta link up five different data sources just to map out the U.S. economy. We've loaded up 19 tools that cover everything from raw time series pulls to regional data and the full FRED taxonomy.
Retrieve Time Series Data
- You can pull the actual data values for any specified FRED series ID using
get_observations, and you can filter those results by date range or adjust units. - You can use
get_seriesto pull the metadata and details for a single series ID, like GDP or the unemployment rate. - If you're tracking what's been updated,
get_series_updateslists all recently maintained FRED series. - To do vintage analysis,
get_vintage_datesgets the historical revision dates for a series.
Search and Find Series
- You can search over 816,000 economic time series using
search_serieswith keywords, frequency, or popularity. - You can find all series matching specific criteria by using
get_series_by_tags. - To browse the subject matter,
search_tagslets you search or look through the full library of FRED tags. - If you need to discover what data falls under a topic,
get_categoryretrieves a FRED category using a specific ID (like Money/Banking), andget_category_childrenfinds the child categories under a given parent ID. get_category_serieslists all available time series that fall within a specific category, letting you filter by units or frequency.get_category_tagsretrieves all tags associated with a data category.
Map Regional Data
get_geo_shapesgets the geographic shape files—stuff like BEA, MSA, or state boundaries—you need for mapping regional data.get_series_groupdetermines the group ID for a series, which you gotta use to pull cross-sectional regional data withget_regional_data.
Track Economic Releases
- You can list all available economic data releases tracked by FRED using
list_releases. get_release_dateslists upcoming and past dates for major economic data reports, giving you a full economic calendar.- You can get detailed info about a single report, like the CPI report, using
get_release. get_release_seriespulls every single series that was published within a specific economic data release.list_sourcesretrieves a list of all official data sources contributing data to FRED.
Discover Everything Else
get_series_by_tagsfinds all series that match specific, user-defined tags (like 'usa;gdp').search_seriessearches over 816,000 economic time series using keywords, returning title, frequency, and popularity.search_tagssearches or browses the full library of FRED tags to discover series by subject matter.list_sourcesretrieves a list of all official data sources that contribute data to FRED.
How FRED Full Access MCP Works
- 1 Start by using a discovery tool like
search_tagsorget_categoryto define the scope of the data you need. - 2 Next, use the appropriate tool—such as
get_series_by_tagsorget_regional_data—to narrow the scope and identify the correct series IDs. - 3 Finally, pass the specific series ID to
get_observationsto pull the actual, cleaned data values for your analysis.
The bottom line is, you use discovery tools first to find the right data, then you pull the data itself.
Who Is FRED Full Access MCP For?
Macroeconomists and data analysts who need to build complete, multi-source economic models. This is for people who wake up and think, 'I need to correlate regional housing starts with national unemployment and historical inflation.' You're the one who can't afford to spend half a day manually cross-referencing data from five different FRED API endpoints.
Uses get_regional_data to compare state-by-state trends in GDP and employment, then uses get_observations to pull the raw numbers for modeling.
Uses get_vintage_dates and get_series to analyze how historical revisions affect current metrics, ensuring the data used in simulations is accurate.
Uses get_series_by_tags to quickly gather all relevant series related to a topic (e.g., 'mortgage') and then builds a predictive model on the resulting time series.
What Changes When You Connect
- You get complete data coverage without switching tools. Instead of needing separate endpoints for regional data, tags, and categories, the
get_regional_datatool handles it all using a unified framework. - The
search_tagstool lets you find data by concept. You don't need the exact series ID; you just search for 'housing' or 'inflation' and get a list of related series. - It saves time on data quality checks. Using
get_vintage_dateslets you see when a series was last revised, which is critical when running historical simulations or backtesting models. - You can model across geography and time easily. The
get_regional_datatool pulls cross-sectional data for multiple states or counties simultaneously, not just one area at a time. - You never miss a data point. By using
get_release_datesandget_release_series, your agent can automatically track the entire economic calendar, ensuring your reports are always based on the latest published figures. - You can find the most popular data instantly. The
search_seriestool lets you sort results by popularity, guiding your agent immediately to the most-used indicators like GDP or UNRATE.
Real-World Use Cases
Building a National Economic Dashboard
The user needs to display unemployment rates (UNRATE) for every state. Instead of running 50 separate API calls, the agent uses get_series_group to find the regional group ID, then runs get_regional_data to pull all state data in one go. The final step is piping that data to get_observations for visualization.
Investigating the Impact of a Rate Change
A user wants to see if changes in the Fed Funds Rate (FEDFUNDS) correlate with mortgage rates over time. They use get_series to confirm the IDs for both series, then get_observations to retrieve the time series data for comparison and correlation analysis.
Forecasting Housing Market Trends
The agent first uses search_tags to find all series tagged 'housing'. This returns a list of relevant series. The agent then uses get_observations on the top candidates to gather the time series data needed to build a predictive housing model.
Auditing Data Sources for Reports
A compliance officer needs to know which data sources were used in last quarter's report. They use list_releases to get a list of major reports, and then get_release_series to audit every single series included in those past reports.
The Tradeoffs
Searching by Name Only
Asking the agent, 'Give me data on the economy.' This is too vague and the agent might return an unusable list of general categories.
→
Use search_tags first. Search for 'macroeconomics' or 'employment'. This limits the search space and helps the agent select the correct, targeted series IDs before calling get_observations.
Ignoring Regional Context
Asking for 'unemployment data' and getting only the national average. This misses state-level variation, which is often the most critical detail.
→
You must use get_series_group followed by get_regional_data. This ensures the data is pulled cross-sectionally for multiple geographic units, not just the national average.
Over-relying on single tools
Calling get_series for a series ID, and then stopping. You only get metadata, not the actual data points needed for analysis.
→
Always follow up get_series with get_observations. The former provides the ID and context; the latter provides the actionable, time-stamped data.
When It Fits, When It Doesn't
Use this server if your analysis requires cross-domain, longitudinal U.S. economic data. Specifically, if you need to compare state-by-state trends (use get_regional_data), track data changes over time (use get_vintage_dates), or gather data based on a conceptual keyword rather than a known ID (use search_tags).
Don't use this if you only need simple data retrieval for a single, known metric. In that case, get_observations is sufficient, but you'll still need the preceding discovery steps. It's designed for complexity, not simplicity.
Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by FRED. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.
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Works with Claude, ChatGPT, Cursor, and more
The Model Context Protocol standardizes how applications expose capabilities to LLMs. Instead of operating in isolation, your AI gains direct access to external platforms, live data, and real-world actions through secure, standardized connections.
This server provides 19 capabilities that interface natively with Claude, ChatGPT, Cursor, and any MCP client. No middleware. No custom integration required.
Available Capabilities
Manually compiling U.S. economic data is a nightmare of tabs and clicks.
Today, pulling a full macroeconomic picture means logging into the Fed website, navigating the taxonomy tree, filtering by state, and then cross-referencing different reports. You spend hours copy-pasting data into spreadsheets and manually checking for data revisions. It’s a painful, multi-step process.
With this server, you just ask your agent for the correlation between housing starts and local job growth. It runs `get_series_by_tags` to find the series, then `get_regional_data` to map the states, and finally `get_observations` to pull the data. You get the final, structured dataset immediately.
The FRED Full Access MCP Server: 19 Tools for Economic Intelligence
Forget manually tracking every release date or checking every data source. Your agent uses `get_release_dates` to build a complete calendar and `get_release_series` to pull every single indicator published on a given day. It also uses `get_series_updates` to ensure you're using the latest version of the data.
It’s the difference between building a report piece by piece and having the entire, fully sourced dataset ready to go. The data integrity and breadth are automatic.
Common Questions About FRED Full Access MCP
How do I find all data related to the housing market using get_series_by_tags? +
Use get_series_by_tags and pass the tag 'housing'. This returns a list of every series matching that tag, saving you from manually browsing the entire category tree.
What is the difference between search_series and search_tags? +
search_series lets you find series by keyword, title, or frequency. search_tags lets you find series by conceptual tags, which is better for broad, academic research.
Do I need to use get_series_group before get_regional_data? +
Yes. You must use get_series_group first. This determines the correct group ID needed to pull cross-sectional regional data.
How do I check if a series has been revised historically? (get_vintage_dates) +
Call get_vintage_dates with the series ID. This returns the dates when the series was last historically revised, which is crucial for accurate historical analysis.
Can I get the data for a specific state using get_observations? +
No. get_observations pulls data for a single series ID. You must first use get_regional_data (which requires a group ID from get_series_group) to pull cross-sectional data for all states.
How do I use get_observations to filter data by a specific date range? +
You pass the start and end dates as parameters to get_observations. You just need to specify the date range you want, and it pulls the values for that period. This is crucial for building historical trend lines or comparing quarters.
What is the difference between get_category and get_category_children? +
get_category retrieves a single category using its ID. get_category_children, however, explores the hierarchy, starting from a root ID (like 0), to list all subcategories. Think of it like browsing a file folder structure.
If I want to see all data releases for a specific economic indicator, should I use get_release_series or get_release_dates? +
Use get_release_series. This tool pulls all the individual series (like UNRATE or CPIAUCSL) that were part of a given release. If you only need the release calendar dates, then get_release_dates is the right choice.
Why choose the Full server instead of individual ones? +
The Full server bundles all 19 tools from all 5 domain-specific servers in a single integration. Ideal for multi-disciplinary AI agents that need to combine time series data with regional analysis, release monitoring, and tag-based discovery — all in one query session.
Use it with your favorite AI tools
Connect this server to Cursor, Claude, VS Code, and more.
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