FRED Categories MCP. Map the full structure of economic indicators.
Gemini Works with every AI agent you already use
…and any MCP-compatible client
Just plug in your AI agents and start using Vinkius.
FRED Categories — Economic Data Taxonomy. This server lets your AI agent navigate the complete FRED taxonomy tree. You can drill down from top-level domains (like Money & Banking or Prices) to find thousands of specific time-series data series.
Use `get_category_children` to map the full economic data landscape, or `get_category_tags` to filter series by specific dimensions like frequency or unit.
What your AI agents can do
Get category
Retrieves details for a specific FRED category using its unique ID.
Get category children
Maps the category hierarchy by listing the child categories under a given parent ID.
Get category series
Gets a list of data series within a category, supporting filters for frequency, units, and tags.
Use get_category_children to recursively explore and map all sub-categories under a given FRED category ID.
Run get_category_series to retrieve a list of data series, allowing filtering by frequency, units, and specific tags.
Call get_category_tags to list all available metadata tags for a category, which helps in filtering series results.
Use get_category to quickly find details for key, high-level FRED category IDs (e.g., Money, Banking & Finance).
Ask AI about this MCP
Supported MCP Clients
Gemini Waiting for input…
FRED Categories: 4 Tools for Data Discovery
These tools allow your AI agent to map the full economic data taxonomy, discover available series, and validate data dimensions in FRED.
019d759eget category
Retrieves details for a specific FRED category using its unique ID.
019d759eget category children
Maps the category hierarchy by listing the child categories under a given parent ID.
019d759eget category series
Gets a list of data series within a category, supporting filters for frequency, units, and tags.
019d759eget category tags
Lists the metadata tags for a category, helping the user understand data filtering options.
Choose How to Get Started
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.
- Import from OpenAPI, Swagger, or YAML specs
- Create Agent Skills with progressive disclosure
- Deploy to edge with MCPFusion framework
- Built in DLP, auth, and compliance on every call
- Real time usage dashboard and cost metering
- Publish to catalog or keep private
Make Your AI Do More
Start with FRED Categories — Economic Data Taxonomy, then connect any of our 4,700+ other servers whenever your AI needs more. One click, no limits.
- Use this MCP plus 4,700+ others, all in one place
- Add new capabilities to your AI anytime you want
- Every connection is secured and compliant automatically
- Track usage and costs across all your servers
- Works with Claude, ChatGPT, Cursor, and more
- New servers added to the catalog every week
What you can do with this MCP connector
FRED Categories - Economic Data Taxonomy
This server lets your AI agent navigate the whole FRED data tree. You don't have to wade through thousands of data pages to find what you need. You map the structure first, figuring out exactly what data exists and how it's organized.
Your agent uses get_category_children to recursively map all sub-categories under a given FRED category ID, letting you explore the full economic data landscape. You can quickly pull details for key, high-level FRED category IDs using get_category.
To find specific data series, run get_category_series on a category. This pulls a list of series and lets you filter results by frequency, units, or specific tags. You can check out all the metadata tags available for a category by calling get_category_tags, which helps you know what filters apply to the data.
FRED organizes data into major groups, like Money, Banking & Finance (32991), Population & Employment (10), National Accounts (32992), Production & Business (1), Prices (32455), International (32263), and Academic Data (33060).
By using get_category_children, you can drill down from these top-level domains to the deepest sub-categories. For example, if you want data on wages, you first map the 'Population & Employment' category, then follow the child IDs until you land on 'Wages'.
Once you're in the right category, you use get_category_series to get the specific data. You'll get a list of series, and you can narrow it down by setting parameters for frequency (like 'annual' or 'quarterly'), units (like 'USD'), or using specific tags. The get_category_tags tool shows you every available dimension for filtering, so you know exactly what filters work for the series you're looking at.
Need to look up a major domain's details quickly? Just feed the ID into get_category, and you'll get the full rundown. It's built for agents, so your AI client handles the whole process. You just tell it what you need, and it navigates the taxonomy tree for you.
How FRED Categories MCP Works
- 1 First, ask the agent to run
get_category_childrenstarting from the root (0) to map the top-level economic domains. - 2 Next, select a domain of interest (e.g., Population & Employment) and pass its ID to
get_category_seriesto pull available data series. - 3 Finally, use
get_category_tagson the chosen category ID to understand the available filters (tags) for those series, narrowing the search.
The bottom line is that the agent processes the category structure first, then pulls data series, and finally refines the data using available tags.
Who Is FRED Categories MCP For?
Data scientists, quantitative analysts, and financial researchers who need to know what data exists before they can analyze it. If your job involves finding economic indicators—from inflation to employment—and you can't easily map the source taxonomy, this server is for you.
Uses the server to map out required data series (e.g., CPI, wage growth) by traversing the taxonomy, ensuring all necessary metrics are included in a single data pull.
Runs get_category_children to explore the full scope of FRED's data, discovering unexpected series or entire domains that might inform a new research paper.
Implements get_category_tags to build robust filtering logic for data pipelines, ensuring data pulls are restricted only to valid units (e.g., percentage vs. raw count).
What Changes When You Connect
- Discover data scope: Use
get_category_childrento map the entire FRED taxonomy tree, eliminating the need to guess which category holds a specific metric. - Filter precisely:
get_category_tagslets you see the exact available data dimensions—like annual vs. monthly—so you can filter series correctly before running a query. - Target specific data:
get_category_seriesretrieves a list of series within a category, letting you pull hundreds of indicators in one structured call. - Systematic exploration: Instead of clicking through dashboards, your agent runs
get_category_childrenandget_category_seriessequentially to build a complete map of available data. - Fast context building: The server helps your agent understand the relationships between data types, allowing it to guide you to the right indicators without manual searching.
Real-World Use Cases
A researcher needs to compare wage growth across different sectors.
The researcher asks their agent for 'wage growth data.' The agent first runs get_category_children to locate the 'Employment' domain. It then uses get_category_series to pull all wage-related series and finally uses get_category_tags to ensure the data is filtered for the correct unit (e.g., percentage change).
A finance team needs to build a model incorporating national accounts data.
The agent starts by calling get_category for the 'National Accounts' ID. It then uses get_category_children to find sub-domains like 'GDP Components.' This structured mapping ensures no critical component (like investment or consumption) is missed in the model build.
A student needs to understand all available price indices in the US.
The student prompts for 'inflation data.' The agent runs get_category_children to isolate the 'Prices' category. It then uses get_category_series to list all CPI, PPI, and other price indices available, providing a comprehensive list for the project.
A data team needs to validate data sources before integration.
The team uses the server to first run get_category_tags on a high-level category. This reveals all possible data dimensions (e.g., 'real', 'nominal', 'USD'). This knowledge prevents the data pipeline from failing later due to an unsupported unit or tag.
The Tradeoffs
Treating FRED like a simple search bar
Asking the agent to just 'find inflation data' without specifying the domain. The agent might get a massive, unmanageable list of results, wasting time and failing to provide structured context.
→
Instead, use get_category_children first to narrow the search to the 'Prices' domain. Then, use get_category_series with filters to pull only the specific indices needed.
Forgetting the data dimensions
Pulling a series list without checking the available units or frequency. The resulting data might be in raw counts when you needed percentage changes, breaking the analysis.
→
Always run get_category_tags on the category ID. This confirms if the data is available in 'percent' or 'annual' format before you run get_category_series.
Skipping the hierarchy map
Trying to find 'Consumer Confidence' by just searching. You might find the series, but you won't know if it belongs to the 'Prices' domain or the 'Employment' domain, causing confusion in documentation.
→
Start with get_category_children to map the entire domain. This establishes the correct parent/child relationships for the indicator, ensuring accuracy.
When It Fits, When It Doesn't
Use this server if your primary goal is data discovery: you don't know the exact indicator ID, but you know the general topic (e.g., 'labor market' or 'global trade'). The flow must be: 1. get_category_children (map top domains); 2. get_category_series (find specific series); 3. get_category_tags (validate filtering options).
Don't use this if you already know the precise FRED ID and are only pulling the data. For that, use a direct data retrieval tool. This server is for taxonomy and metadata—it tells you what data exists and how it's categorized, not the data itself.
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.
VINKIUS INFRASTRUCTURE
Cloud Hosted
Managed infra
V8 Isolated
Sandboxed per request
Zero-Trust Proxy
No stored credentials
DLP Enforced
Policy on every call
GDPR Compliant
EU data residency
Token Compression
~60% cost reduction
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 4 capabilities that interface natively with Claude, ChatGPT, Cursor, and any MCP client. No middleware. No custom integration required.
Available Capabilities
Sifting through FRED's thousands of indicators feels like reading a massive, unindexed textbook.
Today, finding a specific metric means navigating the FRED website's deep, multi-level menus. You click 'Economy,' then 'Prices,' then maybe 'Consumer Goods,' and finally, you find the index. If you miss a sub-category or forget to check the available tags, your data pull fails or returns irrelevant data.
With this MCP server, your agent maps the entire structure first. It uses `get_category_children` to see the full domain map, and then `get_category_series` to pull exactly what you need. You get a structured list of indicators, categorized and ready to use, without clicking anything.
FRED Categories MCP Server: Map the full structure of economic indicators.
The manual process of cross-referencing different data sources for tags, units, and category IDs is slow and prone to human error. You have to copy-paste IDs and manually check documentation to confirm if a series is 'annual' or 'monthly.'
Now, the agent runs `get_category_tags` and `get_category_series` in sequence. It builds a validated list of available dimensions and series, eliminating the guesswork and giving you a clean, structured data manifest.
Common Questions About FRED Categories MCP
How do I start navigating the FRED taxonomy using get_category_children? +
Start by calling get_category_children with the root ID (0). This maps all the main economic domains. After that, you can drill down into specific domains like 'Prices' (32455) to see its sub-categories.
Can I use get_category_series to filter by frequency? +
Yes, get_category_series supports filtering. You pass the category ID and specify the required frequency (e.g., annual, monthly) to narrow the list of available series.
What is the best way to find all available data units for a category? +
Use the get_category_tags tool. This tool reads the metadata for a category and lists all valid tags, including units and dimensions, ensuring your data is consistent.
What does the get_category tool do? +
The get_category tool retrieves detailed information for a single, known FRED category ID, giving you specific metadata about that domain.
When should I use get_category_tags to understand the data dimensions available in a category? +
You use get_category_tags when you need to know the specific dimensions available for filtering series. This helps narrow down results by factors like 'Industry' or 'Geographic Region' before you fetch the actual time series data.
If I know a category ID, how do I check its parent categories using `get_category_children`? +
The get_category_children tool is designed to explore downward, not upward. To find a category's parent, you'll need to use the get_category tool with the specific ID to get its full metadata and lineage.
Does `get_category_series` support filtering by multiple criteria, like frequency and tags? +
Yes, get_category_series accepts multiple filters. You can combine frequency (e.g., 'monthly') with specific tags to get a highly focused list of series, improving your data discovery.
What is the best way to explore the overall structure of the FRED taxonomy using the available tools? +
Start with get_category_children from the root (0). This gives you all the major branches. Then, loop through those results and use get_category_series on the most promising children to drill down efficiently.
How is FRED organized? +
FRED uses a hierarchical tree of categories. The root (ID 0) branches into 8 domains like Money & Banking, Employment, and Prices. Each domain subdivides into hundreds of subcategories, each containing related series. Start from root and drill down to discover data.
Use it with your favorite AI tools
Connect this server to Cursor, Claude, VS Code, and more.
More in this category
Wolfram Alpha
Solve math, science, and engineering queries with computational intelligence.
StarRocks
High-performance analytical database — manage clusters, tables, and query data via AI.
Fivetran
Manage data movement via Fivetran — monitor connectors and destinations, handle groups, track sync states, and audit users directly from any AI agent.
You might also like
Adzuna Alternative
Search millions of job advertisements and access real-time labor market analytics, salary trends, and regional vacancy data.
Acuity Scheduling Alternative
Manage your Acuity appointments and calendars — audit availability and bookings via AI.
MagicDrip
Run LinkedIn outreach campaigns on autopilot with connection requests, follow-ups, and message sequences that feel personal.