Supercharge your AI with FRED Tags & Sources. Find the exact economic series ID you need.
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FRED Tags & Sources — Data Discovery helps your AI agent pinpoint exact economic data series across all 107 official sources.
Instead of guessing, you search by category, geography, or frequency tags—like 'gdp' or 'monthly'—to find the precise dataset ID needed for analysis.
What your AI can do
Search tags
Allows you to browse or search for metadata tags related to geography, topic, source, or frequency.
Get series by tags
Finds specific FRED series by combining multiple tags, letting you narrow results using required or excluded criteria.
List sources
Provides a complete list of every organization that contributes data to the FRED database.
The MCP retrieves a list of data series that match specific combinations of tags like geography or topic.
It lists every organization, such as the BEA or Treasury, that supplies data to FRED.
The MCP allows you to search across tags for location (europe), subject matter (inflation), or report frequency (quarterly).
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Compatible AI Apps
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FRED Tags & Sources — Data Discovery (3 Tools)
Use these tools to search for metadata tags, list contributing organizations, and discover specific economic series IDs using precise tag combinations.
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Start using FRED Tags & Sources — Data Discovery on VinkiusSearch Tags
Allows you to browse or search for metadata tags related to geography, topic, source, or frequency.
Get Series By Tags
Finds specific FRED series by combining multiple tags, letting you narrow results...
List Sources
Provides a complete list of every organization that contributes data to the FRED...
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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 connection provides 3 powerful capabilities that interface natively with Claude, ChatGPT, Cursor, and other compatible AI platforms. No middleware. No custom integration required.
Manually mapping out economic indicators is a grind.
Today, if you're trying to compare U.S. industrial production with European GDP components, you spend time clicking through tabs and cross-referencing dozens of different source pages. You copy data points, paste them into spreadsheets, and then manually try to figure out which tags apply everywhere—a tedious mess.
With this MCP, your agent handles that mapping automatically. You define the scope (USA, Europe, GDP), and it uses `get_series_by_tags` to return a list of precisely defined series IDs for every data point you care about. It's instant discovery.
Discovering Series with FRED Tags & Sources — Data Discovery
You eliminate the need to visit multiple source websites or cross-reference outdated documentation just to verify a series ID. You can use `search_tags` to confirm 'monthly' is an active tag, and then feed that into your next step.
The result is fully automated data discovery. Your agent doesn't just find the data; it finds the precise address for the data.
What your AI can actually do with this
Need to pull a specific piece of macroeconomics data but don't know its series ID? This MCP is your discovery layer. It lets your agent drill down into FRED’s entire catalog, moving you past simple keyword searches and straight to metadata precision. You can narrow results by combining tags—say, finding every 'gdp' measure that was reported 'monthly' for the 'usa'.
You also get a full list of all organizations contributing data, from the BLS to the Census Bureau, so your agent knows exactly where to look. When you connect this MCP via Vinkius, your AI client can map these complex relationships and retrieve metadata faster than manual browsing ever could.
019d759f-7dc9-701f-a58d-d4c0b1ec5986 Here's how it actually works
The bottom line is your agent uses these tools sequentially—search tags first, then get the specific ID—to build an accurate data pointer.
Start by using the search_tags tool to browse or filter metadata tags, identifying key criteria like 'usa' and 'gdp'.
Next, pass those identified tags into get_series_by_tags to narrow the scope and find series that match all specified conditions.
Finally, run list_sources if you need a full list of contributing organizations for context on where the data originates.
Who is this actually for?
Quant researchers and data engineers who need to ingest large, complex datasets from disparate financial sources. This MCP solves the pain of starting a project by spending days manually mapping out required series IDs.
Determining which combination of tags (e.g., 'usa' + 'inflation') will give the most reliable historical data for modeling.
Building automated pipelines that need to discover and validate series IDs from multiple contributing sources before ingestion.
Quickly exploring the entire breadth of a topic, like listing all data sources related to 'gdp' across different geographies (USA vs. Europe).
What Changes When You Connect
Stop guessing which data source to use. list_sources gives your agent a full directory of all 107 contributing organizations, so you know exactly where the number comes from.
Pinpoint specific datasets instantly. By using get_series_by_tags, you can combine tags like 'usa', 'gdp', and 'quarterly' to find only the exact series ID needed for your analysis.
Filter complex metadata accurately. The search_tags tool lets you browse or filter by any dimension—be it a geographic area, an economic topic, or reporting frequency.
Saves hours of manual searching. Instead of clicking through multiple tabs on the FRED site, your agent handles the multi-tag logic in one go.
Works with massive data sets. This MCP is built to handle large catalogs, meaning you don't hit a wall when looking for rare or niche economic indicators.
See it in action
A financial analyst needs to compare inflation rates.
The agent uses search_tags to confirm the tag 'inflation'. It then calls get_series_by_tags using that tag combined with 'usa' and 'monthly' tags, instantly pulling a list of comparable series IDs for a report.
A data engineer needs to map all possible sources.
The agent runs list_sources first. This gives the engineer a comprehensive inventory of every organization feeding FRED, which is essential for mapping out potential API dependencies in an ETL pipeline.
A researcher wants to find GDP data across continents.
The agent uses search_tags to confirm 'gdp' as a topic tag. It then calls get_series_by_tags, combining 'europe' and 'gdp', letting them compare multiple international datasets in one query.
A developer needs to understand data provenance.
The agent runs list_sources and then uses the output of that list to inform which specific tags must be used with get_series_by_tags, ensuring the resulting dataset is trustworthy.
The honest tradeoffs
Searching only by keyword
Asking your agent, 'Show me all US GDP data.' This relies on simple text matching and might miss related series or include irrelevant results.
Instead, use search_tags to identify the specific tags—'usa', 'gdp', etc.—and then pass those precise constraints into get_series_by_tags. That guarantees accuracy.
Ignoring data source context
Assuming every series comes from one place. This is risky, as a single metric can be calculated differently by the BEA versus the Census Bureau.
Run list_sources first to understand who contributes data. Then, structure your queries using tags that specify both the topic and the source type.
Relying on one tool only
Just calling get_series_by_tags('gdp') without adding frequency or geography. You'll get a massive, unmanageable list of potentially irrelevant series.
Always refine the call by combining tags (e.g., 'usa;gdp+monthly'), keeping your search highly specific and manageable.
When It Fits, When It Doesn't
Use this MCP if your primary blocker is finding the precise metadata pointer for a known economic concept. You need to go from 'I want to compare US CPI vs EU CPI' to 'Here are two specific series IDs.' Don't use it if you already know the exact FRED ID; just plug that ID into your pipeline. Also, don't rely on this MCP if your data needs filtering by time range or column name—it handles discovery, not deep-level data manipulation. Use search_tags for broad exploration, and only call get_series_by_tags when you have defined the necessary tags.
Questions you might have
How do I use get_series_by_tags to limit my search? +
You must provide specific tags. For example, passing a combination like 'usa;gdp+monthly' ensures the agent only returns series matching all three criteria.
Does list_sources tell me if the data is complete? +
The list_sources tool just lists the contributing organizations (like BLS or Census Bureau). It tells you who provides the data, not whether that specific dataset has gaps.
What tags can I use with search_tags? +
You can browse by geography (usa), topic (inflation), source (bea), or frequency (quarterly). This makes it easy to define your search boundaries.
Can get_series_by_tags handle multiple countries? +
Yes. You combine the country codes as tags. For example, you'd tag both 'usa' and 'europe' along with the topic tag to compare them.
What happens when I run `get_series_by_tags` with conflicting or non-existent tags? +
The tool handles this by returning a specific error code and an empty list of results. This allows your AI client to easily detect invalid inputs and adjust the query, preventing pipeline failures.
How do I handle rate limits when frequently using `search_tags`? +
If you hit a limit, wait a short period (a few seconds) before retrying. Your agent should implement an exponential backoff strategy to ensure reliable data retrieval without overloading the API.
What structured output does `list_sources` provide? +
It outputs a clear, machine-readable list of all contributing organizations. Each result includes the source name and often a brief description, making it easy for your agent to build a definitive roster.
Does `get_series_by_tags` require an authentication key? +
Yes, you'll need proper credentials configured in your MCP environment. The connection must be authenticated before the tool can successfully query and pull data from FRED.
What is the difference between tags and categories? +
Categories are a strict hierarchy (one parent, many children). Tags are flat labels — each series can have many tags across dimensions: geography (usa), frequency (monthly), topic (gdp), source (bls). Combine tags for powerful cross-dimensional filtering.
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